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and (b) decide whether the following assertion is supported by the release: \"In September 2026, the unemployment rate among core-aged women (aged 25 to 54) rose 0.3 percentage points to 5.3%, and the unemployment rate among core-aged men also rose, by 0.2 percentage points, to 5.8%.\" The estimands are the figures as printed in the release (no re-computation) and a verdict of `supported` (every element of the assertion is stated in the release), `not_supported` (at least one element is contradicted by the release) or `not_assessable` (the release does not report the quantity needed to judge it).\n\n## Inputs\n- `data/sources/statcan-daily-lfs-2026-09.txt` — plain-text extract of Statistics Canada, *The Daily*, \"Labour Force Survey, September 2026\", released 2026-10-09 (Catalogue no. 11-001-X), retrieved 2026-10-30T03:25:42Z from https://www150.statcan.gc.ca/n1/daily-quotidien/261009/dq261009a-eng.htm; Statistics Canada Open Licence. The extract contains the provenance header, the release title and date, and the full narrative text from \"Highlights\" to \"Next release\" (section headings preserved as `## …` lines; charts and data tables are not reproduced). Estimates are seasonally adjusted unless the text says otherwise; employment changes are in persons.\n- `data/README.md` — provenance, retrieval times, licence and SHA-256 of the extract.\n\n## Information-availability rules\n- The extract is the only admissible evidence. Do not use remembered values of the Labour Force Survey or any other release.\n- A locator must name the section heading of the release (the `## …` line) under which the figure or sentence appears, e.g. `statcan-daily-lfs-2026-09 | section \"Highlights\"`.\n- Report printed values exactly (sign and decimals as printed; convert \"68,000\" to the integer 68000 with the sign given in the text).\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `unemployment_rate_sep2026` | number | percent | national unemployment rate for September 2026 as printed |\n| `employment_change_sep2026_persons` | integer | persons | monthly change in national employment in September 2026 (negative if employment declined) |\n| `locators` | object | — | {\"unemployment_rate_sep2026\": \"<source id | section heading>\", \"employment_change_sep2026_persons\": \"<…>\"} |\n| `assertion_verdict` | string | — | \"supported\", \"not_supported\" or \"not_assessable\" for the assertion quoted above |\n| `assertion_locator` | string | — | source id and section heading of the sentence(s) that decide the verdict |\n| `assertion_reason` | string | — | one sentence explaining the verdict, quoting the decisive element |\n| `task_id` | string | — | \"F4-dev-01\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (text parsing is acceptable; hard-coding the numbers in the script is acceptable only if the script also prints the matched source lines).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 8\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:45 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 ..\n-rw-r--r-- 1 simon-pierreboucher staff 1373 oct. 29 23:45 README.md\ndrwxr-xr-x 3 simon-pierreboucher staff 96 oct. 29 23:45 sources","is_error":true,"tool_use_id":"toolu_01Wgq3Q78jTg59joCFwS1RYp"}]},"uuid":"fa07d081-03df-467e-92f6-8fc3d75d33a0","timestamp":"2026-10-30T03:47:42.789Z","toolUseResult":"Error: Exit code 1\n# F4-dev-01 — Two headline figures and one assertion from a Statistics Canada release\n\n## Scientific question\nUsing only the provided text of Statistics Canada's *The Daily* release \"Labour Force Survey, September 2026\", (a) extract two headline national figures for September 2026 — the unemployment rate (seasonally adjusted, in percent) and the monthly change in employment (seasonally adjusted, in persons) — each with a locator naming the section of the release where it is stated; and (b) decide whether the following assertion is supported by the release: \"In September 2026, the unemployment rate among core-aged women (aged 25 to 54) rose 0.3 percentage points to 5.3%, and the unemployment rate among core-aged men also rose, by 0.2 percentage points, to 5.8%.\" The estimands are the figures as printed in the release (no re-computation) and a verdict of `supported` (every element of the assertion is stated in the release), `not_supported` (at least one element is contradicted by the release) or `not_assessable` (the release does not report the quantity needed to judge it).\n\n## Inputs\n- `data/sources/statcan-daily-lfs-2026-09.txt` — plain-text extract of Statistics Canada, *The Daily*, \"Labour Force Survey, September 2026\", released 2026-10-09 (Catalogue no. 11-001-X), retrieved 2026-10-30T03:25:42Z from https://www150.statcan.gc.ca/n1/daily-quotidien/261009/dq261009a-eng.htm; Statistics Canada Open Licence. The extract contains the provenance header, the release title and date, and the full narrative text from \"Highlights\" to \"Next release\" (section headings preserved as `## …` lines; charts and data tables are not reproduced). Estimates are seasonally adjusted unless the text says otherwise; employment changes are in persons.\n- `data/README.md` — provenance, retrieval times, licence and SHA-256 of the extract.\n\n## Information-availability rules\n- The extract is the only admissible evidence. Do not use remembered values of the Labour Force Survey or any other release.\n- A locator must name the section heading of the release (the `## …` line) under which the figure or sentence appears, e.g. `statcan-daily-lfs-2026-09 | section \"Highlights\"`.\n- Report printed values exactly (sign and decimals as printed; convert \"68,000\" to the integer 68000 with the sign given in the text).\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `unemployment_rate_sep2026` | number | percent | national unemployment rate for September 2026 as printed |\n| `employment_change_sep2026_persons` | integer | persons | monthly change in national employment in September 2026 (negative if employment declined) |\n| `locators` | object | — | {\"unemployment_rate_sep2026\": \"<source id | section heading>\", \"employment_change_sep2026_persons\": \"<…>\"} |\n| `assertion_verdict` | string | — | \"supported\", \"not_supported\" or \"not_assessable\" for the assertion quoted above |\n| `assertion_locator` | string | — | source id and section heading of the sentence(s) that decide the verdict |\n| `assertion_reason` | string | — | one sentence explaining the verdict, quoting the decisive element |\n| `task_id` | string | — | \"F4-dev-01\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (text parsing is acceptable; hard-coding the numbers in the script is acceptable only if the script also prints the matched source lines).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 8\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:45 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 ..\n-rw-r--r-- 1 simon-pierreboucher staff 1373 oct. 29 23:45 README.md\ndrwxr-xr-x 3 simon-pierreboucher staff 96 oct. 29 23:45 sources","sandboxed":false,"sourceToolAssistantUUID":"8b19d775-ad72-41bf-abf4-b6707f071f57","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0001","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","hash":"6947b59ea3c0bca320d89b3f7a59df602a72756150ed5133acc7c54aee11af91","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Wgq3Q78jTg59joCFwS1RYp","name":"Bash","input":{"command":"cat TASK.md; ls -la data; ls out 2>/dev/null","description":"Read task and list data"}}]}}15{"type":"api-request","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","id":"87d3557f-d213-4872-99e6-377120f4f1c5","timestamp":"2026-10-30T03:47:42.794Z","version":"2.1.314","querySource":"sdk","shapeHash":"a9e7dfc67201f8244ca5c606e0d408b74c6b31c6f4d0becb7dd0545da0346c33","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"65b1442d-7af0-467a-af50-2e42c6e8673e","keep":2,"tail":["6947b59ea3c0bca320d89b3f7a59df602a72756150ed5133acc7c54aee11af91","95a64a34e2d4352640af18c576688faf650ace786366e6011e43de3e101bb2c4"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"66dca3c4f6e77d2f6c94e2d32d851fd994b64daf80e2c35a4f3728caa13a9ee9"}16{"parentUuid":"fa07d081-03df-467e-92f6-8fc3d75d33a0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjhqDsUEqXaVtttgtRZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Y8a7iEQUMbZdmGDT1eSpb4","name":"Bash","input":{"command":"cat data/README.md; wc -l data/README.md data/sources/*; cat -A data/sources/statcan-daily-lfs-2026-09.txt | head -300; shasum -a 256 data/sources/*","description":"Read the source and README"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2140,"cache_read_input_tokens":2234,"output_tokens":142,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2140},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXjhpx14VewEkW1LV2LJ","requestRef":"87d3557f-d213-4872-99e6-377120f4f1c5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"811fdade-e947-474f-aedd-71049093daa7","timestamp":"2026-10-30T03:47:44.323Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0001","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"811fdade-e947-474f-aedd-71049093daa7","isSidechain":false,"promptId":"3f4ad405-5b99-40b2-b251-59f460202cf7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Y8a7iEQUMbZdmGDT1eSpb4","type":"tool_result","content":"# Data provenance — F4-dev-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/statcan-daily-lfs-2026-09.txt` | Statistics Canada (2026-10-09). The Daily — Labour Force Survey, September 2026. 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No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n 9 data/README.md\n 164 data/sources/statcan-daily-lfs-2026-09.txt\n 173 total\ncat: illegal option -- A\nusage: cat [-belnstuv] [file ...]\n7ea5fec57c87874ff5ee09a8304927a1aaed5a1837db5312fab9f7a2d5f2d0bb data/sources/statcan-daily-lfs-2026-09.txt","is_error":false}]},"uuid":"75fdde81-661b-40b8-98b5-ca8df7576490","timestamp":"2026-10-30T03:47:44.385Z","toolUseResult":{"stdout":"# Data provenance — F4-dev-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/statcan-daily-lfs-2026-09.txt` | Statistics Canada (2026-10-09). The Daily — Labour Force Survey, September 2026. Catalogue no. 11-001-X. https://www150.statcan.gc.ca/n1/daily-quotidien/261009/dq261009a-eng.htm | 2026-10-30T03:25:42Z | Statistics Canada Open Licence | 7ea5fec57c87874ff5ee09a8304927a1aaed5a1837db5312fab9f7a2d5f2d0bb |\n\nRights: The Statistics Canada release is reproduced under the Statistics Canada Open Licence (https://www.statcan.gc.ca/en/reference/licence) with attribution. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n 9 data/README.md\n 164 data/sources/statcan-daily-lfs-2026-09.txt\n 173 total\ncat: illegal option -- A\nusage: cat [-belnstuv] [file ...]\n7ea5fec57c87874ff5ee09a8304927a1aaed5a1837db5312fab9f7a2d5f2d0bb data/sources/statcan-daily-lfs-2026-09.txt","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"811fdade-e947-474f-aedd-71049093daa7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0001","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","hash":"96645dbcaa6ba661dfa8ae9c878067e2ae226e5951640dbf61f4be792cdfd903","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Y8a7iEQUMbZdmGDT1eSpb4","name":"Bash","input":{"command":"cat data/README.md; wc -l data/README.md data/sources/*; cat -A data/sources/statcan-daily-lfs-2026-09.txt | head -300; shasum -a 256 data/sources/*","description":"Read the source and README"}}]}}19{"type":"api-request","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","id":"f3cdd2a2-1a7a-4bff-887c-84d06c692ffc","timestamp":"2026-10-30T03:47:44.389Z","version":"2.1.314","querySource":"sdk","shapeHash":"a9e7dfc67201f8244ca5c606e0d408b74c6b31c6f4d0becb7dd0545da0346c33","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"87d3557f-d213-4872-99e6-377120f4f1c5","keep":4,"tail":["96645dbcaa6ba661dfa8ae9c878067e2ae226e5951640dbf61f4be792cdfd903","c6b6109f4051a8d6b45f86338b90e3f1f74b5a148f77c40df99ee161a8b28151"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"65d26d4007010c53274cd94ea952f7317d1d6927079b22d4b1cf05a51c4a1214"}20{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"75fdde81-661b-40b8-98b5-ca8df7576490","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea"}21{"type":"atis-latch","atis":"","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea"}22{"type":"tools-used","tools":["Bash","Read"],"sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea"}23{"parentUuid":"75fdde81-661b-40b8-98b5-ca8df7576490","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjhx58M13mTXuAXr1GR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01X43tcNiDmduo8evnH6LHeu","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/wp29/runs/RP0001/data/sources/statcan-daily-lfs-2026-09.txt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":840,"cache_read_input_tokens":4374,"output_tokens":96,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":840},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXjhwn28eXCqtbX3tcVN","requestRef":"f3cdd2a2-1a7a-4bff-887c-84d06c692ffc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"89581127-5910-4b5d-a60f-4950cc0b3449","timestamp":"2026-10-30T03:47:45.569Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0001","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"89581127-5910-4b5d-a60f-4950cc0b3449","isSidechain":false,"promptId":"3f4ad405-5b99-40b2-b251-59f460202cf7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01X43tcNiDmduo8evnH6LHeu","type":"tool_result","content":"1\tSOURCE EXTRACT — statcan-daily-lfs-2026-09\n2\tTitle: Labour Force Survey, September 2026 (The Daily)\n3\tAuthors: Statistics Canada\n4\tBibliographic record: Statistics Canada, The Daily, 'Labour Force Survey, September 2026', released 2026-10-09, Catalogue no. 11-001-X. URL: https://www150.statcan.gc.ca/n1/daily-quotidien/261009/dq261009a-eng.htm\n5\tRetrieved: https://www150.statcan.gc.ca/n1/daily-quotidien/261009/dq261009a-eng.htm on 2026-10-30T03:25:42Z (SHA-256 of the retrieved HTML: f7b29f3515c2dcdb24ccee2a3aa945160e4589a5ed1529ec81062c7a3e5137ca)\n6\tLicense: Statistics Canada Open Licence (https://www.statcan.gc.ca/en/reference/licence) — reproduced with attribution: 'Source: Statistics Canada, The Daily, Labour Force Survey, September 2026'. This extract does not reproduce the data tables, only the release text.\n7\tExtract contents: release title and date; the full narrative of the release from 'Highlights' to 'Next release' (the headings of Charts, Infographics, Maps and of data Tables 1-12 are kept as they appear, but the charts and tables themselves are not reproduced).\n8\tOmitted: the interactive provincial dashboard at the top of the page, the data tables, the 'Products' and 'Contact information' sections.\n9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n10\t================================================================\n11\t\n12\t# Labour Force Survey, September 2026\n13\t\n14\tStatistics Canada, The Daily, released 2026-10-09\n15\t\n16\t## Highlights\n17\tEmployment declined by 68,000 (-0.3%) in September and the employment rate fell 0.2 percentage points to 60.6%. The unemployment rate increased 0.1 percentage points to 6.5%.\n18\tThe employment decline was concentrated among youth aged 15 to 24 (-48,000; -1.8%) as well as women aged 25 to 54 (-28,000; -0.4%).\n19\tThere were fewer people working in educational services (-35,000; -2.2%), health care and social assistance (-23,000; -0.8%) as well as manufacturing (-13,000; -0.7%). There were more people working in 'other services' (+17,000; +2.1%), which includes repair and maintenance as well as personal or household services.\n20\tEmployment decreased in Quebec (-49,000; -1.1%), British Columbia (-20,000; -0.7%) and Manitoba (-4,400; -0.6%). It increased in Alberta (+23,000; +0.9%), Newfoundland and Labrador (+1,800; +0.7%), and Prince Edward Island (+1,000; +1.0%).\n21\tAverage hourly wages among employees rose 2.3% (+$0.86 to $37.64) on a year-over-year basis in September, following growth of 2.0% in August (not seasonally adjusted).\n22\t\n23\t## Employment falls for second consecutive month\n24\tIn September, employment declined by 68,000 (-0.3%), split between full-time work (-35,000; -0.2%) and part-time work (-33,000; -0.9%). The employment decline in September follows a decrease in August (-42,000; -0.2%).\n25\t\n26\t## Chart 1 \n27\t\n28\tEmployment rate decreases in September\n29\t\n30\tPrior to these two consecutive monthly declines, employment had trended up from April to July (+181,000; +0.9%). On a year-over-year basis, employment was up by 95,000 (+0.5%) in September.\n31\tThe employment rate—the proportion of the population aged 15 and older who are employed—decreased by 0.2 percentage points to 60.6% in September, marking a second consecutive monthly decrease. On a year-over-year basis, the employment rate in September was unchanged.\n32\t\n33\t## Infographic 1 \n34\t\n35\tEmployment rate by age group, September 2026\n36\t\n37\t## Downward trend in the number of public sector employees\n38\tIn September, the number of public sector employees declined by 70,000 (-1.5%). This was the fourth consecutive monthly decline for the sector. On a year-over-year basis, the number of public sector employees was down by 119,000 (-2.6%), with most of the decline attributable to employment in educational services.\n39\tThe number of private sector employees was little changed for a second consecutive month in September but it was up by 163,000 (+1.2%) compared with 12 months earlier.\n40\tSelf-employment edged down in September (-23,000; -0.8%). This follows an upward trend from April to July. On a year-over-year basis, self-employment was up by 51,000 (+1.9%).\n41\t\n42\t## Unemployment rate increases to 6.5%\n43\tThe unemployment rate rose 0.1 percentage points to 6.5% in September. Earlier in the year, it reached a recent peak of 6.9% in April, before declining to 6.4% in July and August. The unemployment rate in September was the same as it was at the start of the year (6.5% in January).\n44\t\n45\t## Chart 2 \n46\t\n47\tUnemployment rate rises to 6.5% in September\n48\t\n49\t## Infographic 2 \n50\t\n51\tUnemployment rate by age group, September 2026\n52\t\n53\tThe layoff rate—representing the proportion of people who were employed in August but had become unemployed in September as a result of a layoff—was 0.7%, similar to the rate observed a year earlier (0.6%) and the average during the period from 2017 to 2019 (0.6%) (not seasonally adjusted).\n54\tThe job-finding rate was 30.6% in September, down from 32.8% 12 months earlier and below the average of 36.5% during the period from 2017 to 2019. This refers to the proportion of people who were unemployed in August and had found a job in September.\n55\tThe labour force participation rate—the proportion of the population aged 15 and older who were employed or looking for work—fell 0.2 percentage points to 64.8% in September. This was the lowest level since December 1997 (when it was also 64.8%), excluding 2020 during the COVID-19 pandemic. On a year-over-year basis, the participation rate was down 0.4 percentage points in September 2026, largely the result of population aging (See text box: The impact of population aging on the declining participation rate).\n56\tThe impact of population aging on the declining participation rate\n57\tThe Canadian population is aging and is projected to continue to exert downward pressure on labour supply in the coming years (See: The labour force in Canada and its regions: Projections to 2036; The impact of aging on labour market participation rates). While older Canadians are participating in the labour market at higher rates relative to previous cohorts, there is nonetheless a drop-off in participation rates at older ages, especially for those aged 65 and older.\n58\tIn September 2026, people aged 65 and older accounted for 23.2% of the working-age population (i.e., those aged 15 and older) in the Labour Force Survey, up from 20.5% in September 2019, prior to the COVID-19 pandemic (see Chart 3). In comparison, this share was 14.8% at the turn of the millennium.\n59\tThe upward trend in the share of people aged 65 and older in the working-age population was briefly paused in 2023 and 2024, as sharp increases in permanent and temporary immigration offset the aging of the population. However, in 2025 and into 2026, the trend towards an aging population resumed.\n60\tThe lower labour force participation rate observed in September 2026, relative to a year earlier and to the pre-pandemic period, can largely be attributed to population aging. Using a method that holds constant the September 2019 age composition of the population, the participation rate in September 2026 would be little changed (-0.1 percentage points) year over year and would be 0.2 percentage points higher than in September 2019 (see Chart 4).\n61\t\n62\t## Chart 3 \n63\t\n64\tProportion of people aged 65 and older in the working-age population\n65\t\n66\t## Chart 4 \n67\t\n68\tLabour force participation rate, holding the age composition constant to September 2019\n69\t\n70\t## Youth employment declines\n71\tEmployment among youth aged 15 to 24 fell (-48,000; -1.8%) for a second consecutive month in September. The cumulative decline over this two-month period (-67,000; -2.4%) offset gains recorded earlier from April to July. The employment decline in September was accompanied by a decline in the number of youth in the labour force (-50,000; -1.6%), and the youth unemployment rate was little changed at 13.0%.\n72\t\n73\t## Unemployment rate up for core-aged women\n74\tAmong core-aged women (25 to 54 years old), the unemployment rate rose 0.3 percentage points to 5.3% in September, as employment fell (-28,000; -0.4%) and the number of women in this age group searching for work increased (+18,000; +4.9%). Despite the monthly increase, the unemployment rate for core-aged women in September was 0.5 percentage points lower than a year earlier and 0.6 percentage points below a recent peak of 5.9% in April.\n75\tAmong core-aged men, employment held steady in September while the number of men in this age group searching for work edged down. As a result, the unemployment rate for core-aged men fell 0.2 percentage points to 5.8% in the month. Compared with the same month last year, the unemployment rate for core-aged men was down 0.3 percentage points in September.\n76\tThe unemployment rate among people aged 55 and older rose 0.2 percentage points to 5.3% in September, as more people in this age group looked for work. On a year-over-year basis, the unemployment rate for this group was little changed in September.\n77\t\n78\t## Fewer people working in educational services, health care and social assistance as well as manufacturing\n79\tEmployment in educational services fell by 35,000 (-2.2%) in September, following seven consecutive months of little change. Compared with September 2025, employment in educational services was down by 67,000 (-4.2%). The year-over-year decrease was concentrated in Quebec (-27,000; -7.0%), Ontario (-19,000; -3.2%) and Alberta (-17,000; -9.0%).\n80\t\n81\t## Chart 5 \n82\t\n83\tEmployment change by industry, September 2026\n84\t\n85\tEmployment in health care and social assistance fell by 23,000 (-0.8%) in September, the first monthly decrease since December 2022. Despite the decline, employment in health care and social assistance was up by 93,000 (+3.2%) in the 12 months to September and was the largest contributor to overall employment growth over that period.\n86\tIn September, employment also fell in manufacturing (-13,000; -0.7%), following an increase of 22,000 (+1.2%) in August. Employment in manufacturing was little changed in September compared with 12 months earlier.\n87\tMeanwhile, employment in 'other services' increased by 17,000 (+2.1%) in September and was up by 32,000 (+4.1%) compared with September 2025. This industry includes repair and maintenance as well as personal or household services.\n88\t\n89\t## Employment declines in Quebec and British Columbia, increases in Alberta\n90\tEmployment in Quebec fell by 49,000 (-1.1%) in September, following a decrease of 19,000 (-0.4%) in August. Employment in the province has trended down since the start of the year, with a cumulative decline of 130,000 (-2.8%) from January to September. The unemployment rate in Quebec was 6.0% in September, up 0.4 percentage points in the month and up 0.8 percentage points from January.\n91\t\n92\t## Map 1 \n93\t\n94\tUnemployment rate by province and territory, September 2026\n95\t\n96\tIn British Columbia, employment fell by 20,000 (-0.7%) in September, marking the first monthly decrease since March for the province. The employment rate in British Columbia fell 0.4 percentage points to 60.5% while the unemployment rate was little changed at 6.4%.\n97\tEmployment in Ontario edged down (-20,000; -0.2%) in September but was up by 86,000 (+1.1%) compared with 12 months earlier. The unemployment rate in Ontario was little changed at 7.0% in September but was down 0.9 percentage points from 12 months earlier.\n98\tOn the other hand, employment in Alberta rose by 23,000 (+0.9%) in September and was up by 76,000 (+2.9%) compared with a year earlier. The unemployment rate in the province was 6.4% in September, down both in the month (-0.4 percentage points) and on a year-over-year basis (-1.3 percentage points).\n99\t\n100\tTable 1\n101\tLabour force characteristics by age group and gender^, seasonally adjusted\n102\t\n103\tTable 2\n104\tEmployment by class of worker and industry, seasonally adjusted\n105\t\n106\tTable 3\n107\tLabour force characteristics by province, seasonally adjusted\n108\t\n109\tTable 4\n110\tLabour force characteristics by province, age group and gender^, seasonally adjusted (Newfoundland and Labrador, Prince Edward Island, Nova Scotia, and New Brunswick)\n111\t\n112\tTable 5\n113\tLabour force characteristics by province, age group and gender^, seasonally adjusted (Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia)\n114\t\n115\tTable 6\n116\tEmployment by province and industry, seasonally adjusted\n117\t\n118\tTable 7\n119\tLabour force characteristics by census metropolitan area, three-month moving average, seasonally adjusted\n120\t\n121\tTable 8\n122\tLabour force characteristics by Montréal, Toronto and Vancouver census metropolitan areas, monthly, seasonally adjusted\n123\t\n124\tTable 9\n125\tLabour force characteristics by province and economic region,^ three-month moving average ending in September 2025 and September 2026, unadjusted for seasonality\n126\t\n127\tTable 10\n128\tLabour force characteristics by territory, three-month moving average, seasonally adjusted\n129\t\n130\tTable 11\n131\tAverage usual hours and wages of employees by selected characteristics, unadjusted for seasonality\n132\t\n133\tTable 12\n134\tRegional unemployment rates used by the Employment Insurance program,^ three-month moving average, seasonally adjusted\n135\t\n136\t## Sustainable Development Goals\n137\tOn January 1, 2016, the world officially began implementation of the 2030 Agenda for Sustainable Development—the United Nations' transformative plan of action that addresses urgent global challenges over the next 15 years. The plan is based on 17 specific sustainable development goals.\n138\tThe Labour Force Survey is an example of how Statistics Canada supports the reporting on the Global Goals for Sustainable Development. This release will be used in helping to measure the following goals:\n139\t\n140\t### Note to readers\n141\t\n142\tThe Labour Force Survey (LFS) estimates for September reflect labour market conditions during the reference week of September 13 to 19, 2026.\n143\tThe sample size of the LFS is approximately 65,000 households, representing over 100,000 respondents each month. For more information, see the Guide to the Labour Force Survey.\n144\tThis analysis focuses on differences between estimates that are statistically significant at the 68% confidence level. Monthly estimates may show more sampling variability than trends observed over longer periods. For more information, see \"Interpreting Monthly Changes in Employment from the Labour Force Survey.\"\n145\tLFS estimates at the Canada level do not include the territories.\n146\tThe LFS estimates are the first in a series of labour market indicators released by Statistics Canada, which includes indicators from programs such as the Survey of Employment, Payrolls and Hours (SEPH); Employment Insurance Statistics; and the Job Vacancy and Wage Survey. For more information on the conceptual differences between employment measures from the LFS and those from the SEPH, refer to section 8 of the Guide to the Labour Force Survey.\n147\tThe employment rate is the number of employed people as a percentage of the population aged 15 years and older. The rate for a particular group (for example, youth aged 15 to 24 years) is the number employed in that group as a percentage of the population for that group.\n148\tThe unemployment rate is the number of unemployed people as a percentage of the labour force (employed and unemployed).\n149\tThe participation rate is the number of employed and unemployed people as a percentage of the population aged 15 years and older.\n150\tFull-time employment consists of persons who usually work 30 hours or more per week at their main or only job.\n151\tPart-time employment consists of persons who usually work less than 30 hours per week at their main or only job.\n152\tTotal hours worked refers to the number of hours actually worked at the main job by the respondent during the reference week, including paid and unpaid hours. These hours reflect temporary decreases or increases in work hours (for example, hours lost due to illness, vacation, holidays or weather; or more hours worked due to overtime).\n153\tThis release refers to the gender of a person. The category \"men\" includes men, as well as some non-binary persons. The category \"women\" includes women, as well as some non-binary persons. Given that the non-binary population is small, data aggregation to a two-category gender variable is necessary to protect the confidentiality of responses provided.\n154\t\n155\t### Seasonal adjustment\n156\tUnless otherwise stated, estimates presented in this release are seasonally adjusted, which facilitates comparisons by removing the effects of typical seasonal variations. For more information on seasonal adjustment, see Seasonally adjusted data: Concepts and interpretation.\n157\t\n158\t### Population totals in the Labour Force Survey\n159\tThe LFS target population includes all persons aged 15 years and older whose usual place of residence is in Canada, with some exceptions (those living on reserves, full-time members of the regular Armed Forces and persons living in institutions). The target population includes temporary residents—that is, those with a valid work or study permit, their families, and refugee claimants—as well as permanent residents (landed immigrants) and the Canadian-born.\n160\tInformation gathered from LFS respondents is weighted to represent the survey target population using population calibration totals. These totals are updated each month, using the most recently available information on population changes derived from Canada's official population estimates, with minor adjustments being made to reflect the LFS target population.\n161\tWhile the LFS population totals are generally aligned with official demographic estimates, the official estimates should be considered the official measure of population change in Canada. More information on how population totals in the LFS are calculated can be found in the article \"Interpreting population totals from the Labour Force Survey.\"\n162\t\n163\t### Next release\n164\tThe next release of the LFS will be on November 6, 2026. October 2026 data will reflect labour market conditions during the week of October 11 to 17.\n165\t"}]},"uuid":"e230383f-c9b6-4299-be12-9390021b5c71","timestamp":"2026-10-30T03:47:45.576Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/wp29/runs/RP0001/data/sources/statcan-daily-lfs-2026-09.txt","content":"SOURCE EXTRACT — statcan-daily-lfs-2026-09\nTitle: Labour Force Survey, September 2026 (The Daily)\nAuthors: Statistics Canada\nBibliographic record: Statistics Canada, The Daily, 'Labour Force Survey, September 2026', released 2026-10-09, Catalogue no. 11-001-X. URL: https://www150.statcan.gc.ca/n1/daily-quotidien/261009/dq261009a-eng.htm\nRetrieved: https://www150.statcan.gc.ca/n1/daily-quotidien/261009/dq261009a-eng.htm on 2026-10-30T03:25:42Z (SHA-256 of the retrieved HTML: f7b29f3515c2dcdb24ccee2a3aa945160e4589a5ed1529ec81062c7a3e5137ca)\nLicense: Statistics Canada Open Licence (https://www.statcan.gc.ca/en/reference/licence) — reproduced with attribution: 'Source: Statistics Canada, The Daily, Labour Force Survey, September 2026'. This extract does not reproduce the data tables, only the release text.\nExtract contents: release title and date; the full narrative of the release from 'Highlights' to 'Next release' (the headings of Charts, Infographics, Maps and of data Tables 1-12 are kept as they appear, but the charts and tables themselves are not reproduced).\nOmitted: the interactive provincial dashboard at the top of the page, the data tables, the 'Products' and 'Contact information' sections.\nRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n================================================================\n\n# Labour Force Survey, September 2026\n\nStatistics Canada, The Daily, released 2026-10-09\n\n## Highlights\nEmployment declined by 68,000 (-0.3%) in September and the employment rate fell 0.2 percentage points to 60.6%. The unemployment rate increased 0.1 percentage points to 6.5%.\nThe employment decline was concentrated among youth aged 15 to 24 (-48,000; -1.8%) as well as women aged 25 to 54 (-28,000; -0.4%).\nThere were fewer people working in educational services (-35,000; -2.2%), health care and social assistance (-23,000; -0.8%) as well as manufacturing (-13,000; -0.7%). There were more people working in 'other services' (+17,000; +2.1%), which includes repair and maintenance as well as personal or household services.\nEmployment decreased in Quebec (-49,000; -1.1%), British Columbia (-20,000; -0.7%) and Manitoba (-4,400; -0.6%). It increased in Alberta (+23,000; +0.9%), Newfoundland and Labrador (+1,800; +0.7%), and Prince Edward Island (+1,000; +1.0%).\nAverage hourly wages among employees rose 2.3% (+$0.86 to $37.64) on a year-over-year basis in September, following growth of 2.0% in August (not seasonally adjusted).\n\n## Employment falls for second consecutive month\nIn September, employment declined by 68,000 (-0.3%), split between full-time work (-35,000; -0.2%) and part-time work (-33,000; -0.9%). The employment decline in September follows a decrease in August (-42,000; -0.2%).\n\n## Chart 1 \n\nEmployment rate decreases in September\n\nPrior to these two consecutive monthly declines, employment had trended up from April to July (+181,000; +0.9%). On a year-over-year basis, employment was up by 95,000 (+0.5%) in September.\nThe employment rate—the proportion of the population aged 15 and older who are employed—decreased by 0.2 percentage points to 60.6% in September, marking a second consecutive monthly decrease. On a year-over-year basis, the employment rate in September was unchanged.\n\n## Infographic 1 \n\nEmployment rate by age group, September 2026\n\n## Downward trend in the number of public sector employees\nIn September, the number of public sector employees declined by 70,000 (-1.5%). This was the fourth consecutive monthly decline for the sector. On a year-over-year basis, the number of public sector employees was down by 119,000 (-2.6%), with most of the decline attributable to employment in educational services.\nThe number of private sector employees was little changed for a second consecutive month in September but it was up by 163,000 (+1.2%) compared with 12 months earlier.\nSelf-employment edged down in September (-23,000; -0.8%). This follows an upward trend from April to July. On a year-over-year basis, self-employment was up by 51,000 (+1.9%).\n\n## Unemployment rate increases to 6.5%\nThe unemployment rate rose 0.1 percentage points to 6.5% in September. Earlier in the year, it reached a recent peak of 6.9% in April, before declining to 6.4% in July and August. The unemployment rate in September was the same as it was at the start of the year (6.5% in January).\n\n## Chart 2 \n\nUnemployment rate rises to 6.5% in September\n\n## Infographic 2 \n\nUnemployment rate by age group, September 2026\n\nThe layoff rate—representing the proportion of people who were employed in August but had become unemployed in September as a result of a layoff—was 0.7%, similar to the rate observed a year earlier (0.6%) and the average during the period from 2017 to 2019 (0.6%) (not seasonally adjusted).\nThe job-finding rate was 30.6% in September, down from 32.8% 12 months earlier and below the average of 36.5% during the period from 2017 to 2019. This refers to the proportion of people who were unemployed in August and had found a job in September.\nThe labour force participation rate—the proportion of the population aged 15 and older who were employed or looking for work—fell 0.2 percentage points to 64.8% in September. This was the lowest level since December 1997 (when it was also 64.8%), excluding 2020 during the COVID-19 pandemic. On a year-over-year basis, the participation rate was down 0.4 percentage points in September 2026, largely the result of population aging (See text box: The impact of population aging on the declining participation rate).\nThe impact of population aging on the declining participation rate\nThe Canadian population is aging and is projected to continue to exert downward pressure on labour supply in the coming years (See: The labour force in Canada and its regions: Projections to 2036; The impact of aging on labour market participation rates). While older Canadians are participating in the labour market at higher rates relative to previous cohorts, there is nonetheless a drop-off in participation rates at older ages, especially for those aged 65 and older.\nIn September 2026, people aged 65 and older accounted for 23.2% of the working-age population (i.e., those aged 15 and older) in the Labour Force Survey, up from 20.5% in September 2019, prior to the COVID-19 pandemic (see Chart 3). In comparison, this share was 14.8% at the turn of the millennium.\nThe upward trend in the share of people aged 65 and older in the working-age population was briefly paused in 2023 and 2024, as sharp increases in permanent and temporary immigration offset the aging of the population. However, in 2025 and into 2026, the trend towards an aging population resumed.\nThe lower labour force participation rate observed in September 2026, relative to a year earlier and to the pre-pandemic period, can largely be attributed to population aging. Using a method that holds constant the September 2019 age composition of the population, the participation rate in September 2026 would be little changed (-0.1 percentage points) year over year and would be 0.2 percentage points higher than in September 2019 (see Chart 4).\n\n## Chart 3 \n\nProportion of people aged 65 and older in the working-age population\n\n## Chart 4 \n\nLabour force participation rate, holding the age composition constant to September 2019\n\n## Youth employment declines\nEmployment among youth aged 15 to 24 fell (-48,000; -1.8%) for a second consecutive month in September. The cumulative decline over this two-month period (-67,000; -2.4%) offset gains recorded earlier from April to July. The employment decline in September was accompanied by a decline in the number of youth in the labour force (-50,000; -1.6%), and the youth unemployment rate was little changed at 13.0%.\n\n## Unemployment rate up for core-aged women\nAmong core-aged women (25 to 54 years old), the unemployment rate rose 0.3 percentage points to 5.3% in September, as employment fell (-28,000; -0.4%) and the number of women in this age group searching for work increased (+18,000; +4.9%). Despite the monthly increase, the unemployment rate for core-aged women in September was 0.5 percentage points lower than a year earlier and 0.6 percentage points below a recent peak of 5.9% in April.\nAmong core-aged men, employment held steady in September while the number of men in this age group searching for work edged down. As a result, the unemployment rate for core-aged men fell 0.2 percentage points to 5.8% in the month. Compared with the same month last year, the unemployment rate for core-aged men was down 0.3 percentage points in September.\nThe unemployment rate among people aged 55 and older rose 0.2 percentage points to 5.3% in September, as more people in this age group looked for work. On a year-over-year basis, the unemployment rate for this group was little changed in September.\n\n## Fewer people working in educational services, health care and social assistance as well as manufacturing\nEmployment in educational services fell by 35,000 (-2.2%) in September, following seven consecutive months of little change. Compared with September 2025, employment in educational services was down by 67,000 (-4.2%). The year-over-year decrease was concentrated in Quebec (-27,000; -7.0%), Ontario (-19,000; -3.2%) and Alberta (-17,000; -9.0%).\n\n## Chart 5 \n\nEmployment change by industry, September 2026\n\nEmployment in health care and social assistance fell by 23,000 (-0.8%) in September, the first monthly decrease since December 2022. Despite the decline, employment in health care and social assistance was up by 93,000 (+3.2%) in the 12 months to September and was the largest contributor to overall employment growth over that period.\nIn September, employment also fell in manufacturing (-13,000; -0.7%), following an increase of 22,000 (+1.2%) in August. Employment in manufacturing was little changed in September compared with 12 months earlier.\nMeanwhile, employment in 'other services' increased by 17,000 (+2.1%) in September and was up by 32,000 (+4.1%) compared with September 2025. This industry includes repair and maintenance as well as personal or household services.\n\n## Employment declines in Quebec and British Columbia, increases in Alberta\nEmployment in Quebec fell by 49,000 (-1.1%) in September, following a decrease of 19,000 (-0.4%) in August. Employment in the province has trended down since the start of the year, with a cumulative decline of 130,000 (-2.8%) from January to September. The unemployment rate in Quebec was 6.0% in September, up 0.4 percentage points in the month and up 0.8 percentage points from January.\n\n## Map 1 \n\nUnemployment rate by province and territory, September 2026\n\nIn British Columbia, employment fell by 20,000 (-0.7%) in September, marking the first monthly decrease since March for the province. The employment rate in British Columbia fell 0.4 percentage points to 60.5% while the unemployment rate was little changed at 6.4%.\nEmployment in Ontario edged down (-20,000; -0.2%) in September but was up by 86,000 (+1.1%) compared with 12 months earlier. The unemployment rate in Ontario was little changed at 7.0% in September but was down 0.9 percentage points from 12 months earlier.\nOn the other hand, employment in Alberta rose by 23,000 (+0.9%) in September and was up by 76,000 (+2.9%) compared with a year earlier. The unemployment rate in the province was 6.4% in September, down both in the month (-0.4 percentage points) and on a year-over-year basis (-1.3 percentage points).\n\nTable 1\nLabour force characteristics by age group and gender^, seasonally adjusted\n\nTable 2\nEmployment by class of worker and industry, seasonally adjusted\n\nTable 3\nLabour force characteristics by province, seasonally adjusted\n\nTable 4\nLabour force characteristics by province, age group and gender^, seasonally adjusted (Newfoundland and Labrador, Prince Edward Island, Nova Scotia, and New Brunswick)\n\nTable 5\nLabour force characteristics by province, age group and gender^, seasonally adjusted (Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia)\n\nTable 6\nEmployment by province and industry, seasonally adjusted\n\nTable 7\nLabour force characteristics by census metropolitan area, three-month moving average, seasonally adjusted\n\nTable 8\nLabour force characteristics by Montréal, Toronto and Vancouver census metropolitan areas, monthly, seasonally adjusted\n\nTable 9\nLabour force characteristics by province and economic region,^ three-month moving average ending in September 2025 and September 2026, unadjusted for seasonality\n\nTable 10\nLabour force characteristics by territory, three-month moving average, seasonally adjusted\n\nTable 11\nAverage usual hours and wages of employees by selected characteristics, unadjusted for seasonality\n\nTable 12\nRegional unemployment rates used by the Employment Insurance program,^ three-month moving average, seasonally adjusted\n\n## Sustainable Development Goals\nOn January 1, 2016, the world officially began implementation of the 2030 Agenda for Sustainable Development—the United Nations' transformative plan of action that addresses urgent global challenges over the next 15 years. The plan is based on 17 specific sustainable development goals.\nThe Labour Force Survey is an example of how Statistics Canada supports the reporting on the Global Goals for Sustainable Development. This release will be used in helping to measure the following goals:\n\n### Note to readers\n\nThe Labour Force Survey (LFS) estimates for September reflect labour market conditions during the reference week of September 13 to 19, 2026.\nThe sample size of the LFS is approximately 65,000 households, representing over 100,000 respondents each month. For more information, see the Guide to the Labour Force Survey.\nThis analysis focuses on differences between estimates that are statistically significant at the 68% confidence level. Monthly estimates may show more sampling variability than trends observed over longer periods. For more information, see \"Interpreting Monthly Changes in Employment from the Labour Force Survey.\"\nLFS estimates at the Canada level do not include the territories.\nThe LFS estimates are the first in a series of labour market indicators released by Statistics Canada, which includes indicators from programs such as the Survey of Employment, Payrolls and Hours (SEPH); Employment Insurance Statistics; and the Job Vacancy and Wage Survey. For more information on the conceptual differences between employment measures from the LFS and those from the SEPH, refer to section 8 of the Guide to the Labour Force Survey.\nThe employment rate is the number of employed people as a percentage of the population aged 15 years and older. The rate for a particular group (for example, youth aged 15 to 24 years) is the number employed in that group as a percentage of the population for that group.\nThe unemployment rate is the number of unemployed people as a percentage of the labour force (employed and unemployed).\nThe participation rate is the number of employed and unemployed people as a percentage of the population aged 15 years and older.\nFull-time employment consists of persons who usually work 30 hours or more per week at their main or only job.\nPart-time employment consists of persons who usually work less than 30 hours per week at their main or only job.\nTotal hours worked refers to the number of hours actually worked at the main job by the respondent during the reference week, including paid and unpaid hours. These hours reflect temporary decreases or increases in work hours (for example, hours lost due to illness, vacation, holidays or weather; or more hours worked due to overtime).\nThis release refers to the gender of a person. The category \"men\" includes men, as well as some non-binary persons. The category \"women\" includes women, as well as some non-binary persons. Given that the non-binary population is small, data aggregation to a two-category gender variable is necessary to protect the confidentiality of responses provided.\n\n### Seasonal adjustment\nUnless otherwise stated, estimates presented in this release are seasonally adjusted, which facilitates comparisons by removing the effects of typical seasonal variations. For more information on seasonal adjustment, see Seasonally adjusted data: Concepts and interpretation.\n\n### Population totals in the Labour Force Survey\nThe LFS target population includes all persons aged 15 years and older whose usual place of residence is in Canada, with some exceptions (those living on reserves, full-time members of the regular Armed Forces and persons living in institutions). The target population includes temporary residents—that is, those with a valid work or study permit, their families, and refugee claimants—as well as permanent residents (landed immigrants) and the Canadian-born.\nInformation gathered from LFS respondents is weighted to represent the survey target population using population calibration totals. These totals are updated each month, using the most recently available information on population changes derived from Canada's official population estimates, with minor adjustments being made to reflect the LFS target population.\nWhile the LFS population totals are generally aligned with official demographic estimates, the official estimates should be considered the official measure of population change in Canada. More information on how population totals in the LFS are calculated can be found in the article \"Interpreting population totals from the Labour Force Survey.\"\n\n### Next release\nThe next release of the LFS will be on November 6, 2026. October 2026 data will reflect labour market conditions during the week of October 11 to 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Now writing the 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-p out && cat > out/analysis.py <<'EOF'\n\"\"\"F4-dev-01: extract headline LFS figures and assess one assertion from the extract.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, re, hashlib\nfrom pathlib import Path\n\nSRC_ID = \"statcan-daily-lfs-2026-09\"\nSRC = Path(\"data/sources\") / f\"{SRC_ID}.txt\"\nREADME = Path(\"data/README.md\")\nEXPECTED_SHA = \"7ea5fec57c87874ff5ee09a8304927a1aaed5a1837db5312fab9f7a2d5f2d0bb\"\n\nraw = SRC.read_bytes()\nsha = hashlib.sha256(raw).hexdigest()\nlines = raw.decode(\"utf-8\").splitlines()\n\n# map each line to the most recent '## ' / '### ' section heading\nsections = []\ncur = None\nfor ln in lines:\n m = re.match(r\"^#{2,3}\\s+(.*?)\\s*$\", ln)\n if m:\n cur = m.group(1).strip()\n sections.append(cur)\n\ndef find(pattern, section=None):\n out = []\n for i, ln in enumerate(lines):\n if (section is None or sections[i] == section):\n m = re.search(pattern, ln)\n if m:\n out.append((i + 1, sections[i], m, ln))\n return out\n\ndef loc(sec):\n return f'{SRC_ID} | section \"{sec}\"'\n\n# (a1) unemployment rate\nur_hits = find(r\"[Tt]he unemployment rate (?:increased|rose) ([\\d.]+) percentage points to ([\\d.]+)%\", \"Highlights\")\nprint(\"Unemployment rate matches (Highlights):\")\nfor h in ur_hits: print(f\" line {h[0]} [{h[1]}]: {h[3]}\")\nur = float(ur_hits[0][2].group(2))\nur_check = find(r\"The unemployment rate rose [\\d.]+ percentage points to ([\\d.]+)% in September\", \"Unemployment rate increases to 6.5%\")\nfor h in ur_check: print(f\" cross-check line {h[0]} [{h[1]}]: {h[3]}\")\nassert all(float(h[2].group(1)) == ur for h in ur_check)\n\n# (a2) employment change\nemp_hits = find(r\"Employment (declined|increased|rose|fell) by ([\\d,]+) \\(([-+][\\d.]+)%\\) in September\", \"Highlights\")\nprint(\"Employment change matches (Highlights):\")\nfor h in emp_hits: print(f\" line {h[0]} [{h[1]}]: {h[3]}\")\nverb, num = emp_hits[0][2].group(1), emp_hits[0][2].group(2)\nsign = -1 if verb in (\"declined\", \"fell\") else 1\nemp = sign * int(num.replace(\",\", \"\"))\nemp_check = find(r\"employment (declined|increased) by ([\\d,]+) \\(\", \"Employment falls for second consecutive month\")\nfor h in emp_check: print(f\" cross-check line {h[0]} [{h[1]}]: {h[3]}\")\nassert all((-1 if h[2].group(1) == \"declined\" else 1) * int(h[2].group(2).replace(\",\", \"\")) == emp for h in emp_check)\n\n# (b) assertion\nASSERT_SEC = \"Unemployment rate up for core-aged women\"\nw = find(r\"core-aged women \\(25 to 54 years old\\), the unemployment rate (rose|fell) ([\\d.]+) percentage points to ([\\d.]+)%\", ASSERT_SEC)\nm_ = find(r\"unemployment rate for core-aged men (rose|fell|increased|decreased) ([\\d.]+) percentage points to ([\\d.]+)%\", ASSERT_SEC)\nprint(\"Assertion evidence:\")\nfor h in w + m_: print(f\" line {h[0]} [{h[1]}]: {h[3]}\")\nwd, wc, wl = w[0][2].group(1), float(w[0][2].group(2)), float(w[0][2].group(3))\nmd, mc, ml = m_[0][2].group(1), float(m_[0][2].group(2)), float(m_[0][2].group(3))\nchecks = {\n \"women direction rose\": wd == \"rose\", \"women change 0.3\": wc == 0.3, \"women level 5.3\": wl == 5.3,\n \"men direction rose\": md in (\"rose\", \"increased\"), \"men change 0.2\": mc == 0.2, \"men level 5.8\": ml == 5.8,\n}\nprint(\"Element checks:\", checks)\nverdict = \"supported\" if all(checks.values()) else \"not_supported\"\nreason = (f'The release confirms the women\\'s figures (rose {wc} points to {wl}%) and the men\\'s magnitude and level, '\n f'but states that \"the unemployment rate for core-aged men {md} {mc} percentage points to {ml}%\", '\n f'contradicting the assertion that it rose.') if verdict == \"not_supported\" else \\\n \"Every element of the assertion is stated in the release.\"\n\ndata_issues = [\n {\"issue\": \"Chart, infographic, map and data tables (Tables 1-12) are not reproduced in the extract; only their headings remain (e.g. empty '## Chart 1 ' headings with trailing spaces).\",\n \"handling\": \"Only narrative text used; figures taken from sentences; trailing whitespace stripped from headings when building locators.\"},\n {\"issue\": \"The 'Sustainable Development Goals' section ends with 'the following goals:' but the list of goals is missing from the extract.\",\n \"handling\": \"Not relevant to the estimands; noted only.\"},\n {\"issue\": \"Headline figures are stated in more than one section (Highlights and a thematic section).\",\n \"handling\": \"Locator gives the Highlights section; the script cross-checks that the thematic-section values are identical (they are).\"},\n]\nif sha != EXPECTED_SHA:\n data_issues.append({\"issue\": f\"SHA-256 of extract ({sha}) differs from README\", \"handling\": \"Reported; proceeded.\"})\n\ndef wc_l(p): return p.read_bytes().count(b\"\\n\")\n\nresults = {\n \"unemployment_rate_sep2026\": ur,\n \"employment_change_sep2026_persons\": emp,\n \"locators\": {\n \"unemployment_rate_sep2026\": loc(ur_hits[0][1]),\n \"employment_change_sep2026_persons\": loc(emp_hits[0][1]),\n },\n \"assertion_verdict\": verdict,\n \"assertion_locator\": loc(ASSERT_SEC),\n \"assertion_reason\": reason,\n \"task_id\": \"F4-dev-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": data_issues,\n \"n_input_rows\": {\"README.md\": wc_l(README), f\"sources/{SRC_ID}.txt\": wc_l(SRC)},\n \"n_analysed\": 3,\n \"random_seed\": None,\n}\nPath(\"out/results.json\").write_text(json.dumps(results, indent=2, ensure_ascii=False) + \"\\n\")\nprint(json.dumps(results, indent=2, ensure_ascii=False))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis 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rate matches (Highlights):\n line 17 [Highlights]: Employment declined by 68,000 (-0.3%) in September and the employment rate fell 0.2 percentage points to 60.6%. The unemployment rate increased 0.1 percentage points to 6.5%.\n cross-check line 43 [Unemployment rate increases to 6.5%]: The unemployment rate rose 0.1 percentage points to 6.5% in September. Earlier in the year, it reached a recent peak of 6.9% in April, before declining to 6.4% in July and August. The unemployment rate in September was the same as it was at the start of the year (6.5% in January).\nEmployment change matches (Highlights):\n line 17 [Highlights]: Employment declined by 68,000 (-0.3%) in September and the employment rate fell 0.2 percentage points to 60.6%. The unemployment rate increased 0.1 percentage points to 6.5%.\n cross-check line 24 [Employment falls for second consecutive month]: In September, employment declined by 68,000 (-0.3%), split between full-time work (-35,000; -0.2%) and part-time work (-33,000; -0.9%). The employment decline in September follows a decrease in August (-42,000; -0.2%).\nAssertion evidence:\n line 74 [Unemployment rate up for core-aged women]: Among core-aged women (25 to 54 years old), the unemployment rate rose 0.3 percentage points to 5.3% in September, as employment fell (-28,000; -0.4%) and the number of women in this age group searching for work increased (+18,000; +4.9%). Despite the monthly increase, the unemployment rate for core-aged women in September was 0.5 percentage points lower than a year earlier and 0.6 percentage points below a recent peak of 5.9% in April.\n line 75 [Unemployment rate up for core-aged women]: Among core-aged men, employment held steady in September while the number of men in this age group searching for work edged down. As a result, the unemployment rate for core-aged men fell 0.2 percentage points to 5.8% in the month. Compared with the same month last year, the unemployment rate for core-aged men was down 0.3 percentage points in September.\nElement checks: {'women direction rose': True, 'women change 0.3': True, 'women level 5.3': True, 'men direction rose': False, 'men change 0.2': True, 'men level 5.8': True}\n{\n \"unemployment_rate_sep2026\": 6.5,\n \"employment_change_sep2026_persons\": -68000,\n \"locators\": {\n \"unemployment_rate_sep2026\": \"statcan-daily-lfs-2026-09 | section \\\"Highlights\\\"\",\n \"employment_change_sep2026_persons\": \"statcan-daily-lfs-2026-09 | section \\\"Highlights\\\"\"\n },\n \"assertion_verdict\": \"not_supported\",\n \"assertion_locator\": \"statcan-daily-lfs-2026-09 | section \\\"Unemployment rate up for core-aged women\\\"\",\n \"assertion_reason\": \"The release confirms the women's figures (rose 0.3 points to 5.3%) and the men's magnitude and level, but states that \\\"the unemployment rate for core-aged men fell 0.2 percentage points to 5.8%\\\", contradicting the assertion that it rose.\",\n \"task_id\": \"F4-dev-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Chart, infographic, map and data tables (Tables 1-12) are not reproduced in the extract; only their headings remain (e.g. empty '## Chart 1 ' headings with trailing spaces).\",\n \"handling\": \"Only narrative text used; figures taken from sentences; trailing whitespace stripped from headings when building locators.\"\n },\n {\n \"issue\": \"The 'Sustainable Development Goals' section ends with 'the following goals:' but the list of goals is missing from the extract.\",\n \"handling\": \"Not relevant to the estimands; noted only.\"\n },\n {\n \"issue\": \"Headline figures are stated in more than one section (Highlights and a thematic section).\",\n \"handling\": \"Locator gives the Highlights section; the script cross-checks that the thematic-section values are identical (they are).\"\n }\n ],\n \"n_input_rows\": {\n \"README.md\": 9,\n \"sources/statcan-daily-lfs-2026-09.txt\": 164\n },\n \"n_analysed\": 3,\n \"random_seed\": null\n}","is_error":false}]},"uuid":"2c3de767-dbe2-4a58-b176-26d381bf620d","timestamp":"2026-10-30T03:48:12.738Z","toolUseResult":{"stdout":"Unemployment rate matches (Highlights):\n line 17 [Highlights]: Employment declined by 68,000 (-0.3%) in September and the employment rate fell 0.2 percentage points to 60.6%. The unemployment rate increased 0.1 percentage points to 6.5%.\n cross-check line 43 [Unemployment rate increases to 6.5%]: The unemployment rate rose 0.1 percentage points to 6.5% in September. Earlier in the year, it reached a recent peak of 6.9% in April, before declining to 6.4% in July and August. The unemployment rate in September was the same as it was at the start of the year (6.5% in January).\nEmployment change matches (Highlights):\n line 17 [Highlights]: Employment declined by 68,000 (-0.3%) in September and the employment rate fell 0.2 percentage points to 60.6%. The unemployment rate increased 0.1 percentage points to 6.5%.\n cross-check line 24 [Employment falls for second consecutive month]: In September, employment declined by 68,000 (-0.3%), split between full-time work (-35,000; -0.2%) and part-time work (-33,000; -0.9%). The employment decline in September follows a decrease in August (-42,000; -0.2%).\nAssertion evidence:\n line 74 [Unemployment rate up for core-aged women]: Among core-aged women (25 to 54 years old), the unemployment rate rose 0.3 percentage points to 5.3% in September, as employment fell (-28,000; -0.4%) and the number of women in this age group searching for work increased (+18,000; +4.9%). Despite the monthly increase, the unemployment rate for core-aged women in September was 0.5 percentage points lower than a year earlier and 0.6 percentage points below a recent peak of 5.9% in April.\n line 75 [Unemployment rate up for core-aged women]: Among core-aged men, employment held steady in September while the number of men in this age group searching for work edged down. As a result, the unemployment rate for core-aged men fell 0.2 percentage points to 5.8% in the month. Compared with the same month last year, the unemployment rate for core-aged men was down 0.3 percentage points in September.\nElement checks: {'women direction rose': True, 'women change 0.3': True, 'women level 5.3': True, 'men direction rose': False, 'men change 0.2': True, 'men level 5.8': True}\n{\n \"unemployment_rate_sep2026\": 6.5,\n \"employment_change_sep2026_persons\": -68000,\n \"locators\": {\n \"unemployment_rate_sep2026\": \"statcan-daily-lfs-2026-09 | section \\\"Highlights\\\"\",\n \"employment_change_sep2026_persons\": \"statcan-daily-lfs-2026-09 | section \\\"Highlights\\\"\"\n },\n \"assertion_verdict\": \"not_supported\",\n \"assertion_locator\": \"statcan-daily-lfs-2026-09 | section \\\"Unemployment rate up for core-aged women\\\"\",\n \"assertion_reason\": \"The release confirms the women's figures (rose 0.3 points to 5.3%) and the men's magnitude and level, but states that \\\"the unemployment rate for core-aged men fell 0.2 percentage points to 5.8%\\\", contradicting the assertion that it rose.\",\n \"task_id\": \"F4-dev-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Chart, infographic, map and data tables (Tables 1-12) are not reproduced in the extract; only their headings remain (e.g. empty '## Chart 1 ' headings with trailing spaces).\",\n \"handling\": \"Only narrative text used; figures taken from sentences; trailing whitespace stripped from headings when building locators.\"\n },\n {\n \"issue\": \"The 'Sustainable Development Goals' section ends with 'the following goals:' but the list of goals is missing from the extract.\",\n \"handling\": \"Not relevant to the estimands; noted only.\"\n },\n {\n \"issue\": \"Headline figures are stated in more than one section (Highlights and a thematic section).\",\n \"handling\": \"Locator gives the Highlights section; the script cross-checks that the thematic-section values are identical (they are).\"\n }\n ],\n \"n_input_rows\": {\n \"README.md\": 9,\n \"sources/statcan-daily-lfs-2026-09.txt\": 164\n },\n \"n_analysed\": 3,\n \"random_seed\": null\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"191dd3b2-c4e3-45fd-8eb1-739675f37772","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0001","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","hash":"580aa8ba6f1d11b68a3f9a6bc82b4dcdec61940d5459c0036b84777cf6fbdb5c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The release says core-aged men's unemployment **fell** 0.2 points to 5.8%, which contradicts the assertion. Now writing the script."},{"type":"tool_use","id":"toolu_01WFJrPr49ysbExATXqE2KPW","name":"Bash","input":{"command":"mkdir -p out && cat > out/analysis.py <<'EOF'\n\"\"\"F4-dev-01: extract headline LFS figures and assess one assertion from the extract.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, re, hashlib\nfrom pathlib import Path\n\nSRC_ID = \"statcan-daily-lfs-2026-09\"\nSRC = Path(\"data/sources\") / f\"{SRC_ID}.txt\"\nREADME = Path(\"data/README.md\")\nEXPECTED_SHA = \"7ea5fec57c87874ff5ee09a8304927a1aaed5a1837db5312fab9f7a2d5f2d0bb\"\n\nraw = SRC.read_bytes()\nsha = hashlib.sha256(raw).hexdigest()\nlines = raw.decode(\"utf-8\").splitlines()\n\n# map each line to the most recent '## ' / '### ' section heading\nsections = []\ncur = None\nfor ln in lines:\n m = re.match(r\"^#{2,3}\\s+(.*?)\\s*$\", ln)\n if m:\n cur = m.group(1).strip()\n sections.append(cur)\n\ndef find(pattern, section=None):\n out = []\n for i, ln in enumerate(lines):\n if (section is None or sections[i] == section):\n m = re.search(pattern, ln)\n if m:\n out.append((i + 1, sections[i], m, ln))\n return out\n\ndef loc(sec):\n return f'{SRC_ID} | section \"{sec}\"'\n\n# (a1) unemployment rate\nur_hits = find(r\"[Tt]he unemployment rate (?:increased|rose) ([\\d.]+) percentage points to ([\\d.]+)%\", \"Highlights\")\nprint(\"Unemployment rate matches (Highlights):\")\nfor h in ur_hits: print(f\" line {h[0]} [{h[1]}]: {h[3]}\")\nur = float(ur_hits[0][2].group(2))\nur_check = find(r\"The unemployment rate rose [\\d.]+ percentage points to ([\\d.]+)% in September\", \"Unemployment rate increases to 6.5%\")\nfor h in ur_check: print(f\" cross-check line {h[0]} [{h[1]}]: {h[3]}\")\nassert all(float(h[2].group(1)) == ur for h in ur_check)\n\n# (a2) employment change\nemp_hits = find(r\"Employment (declined|increased|rose|fell) by ([\\d,]+) \\(([-+][\\d.]+)%\\) in September\", \"Highlights\")\nprint(\"Employment change matches (Highlights):\")\nfor h in emp_hits: print(f\" line {h[0]} [{h[1]}]: {h[3]}\")\nverb, num = emp_hits[0][2].group(1), emp_hits[0][2].group(2)\nsign = -1 if verb in (\"declined\", \"fell\") else 1\nemp = sign * int(num.replace(\",\", \"\"))\nemp_check = find(r\"employment (declined|increased) by ([\\d,]+) \\(\", \"Employment falls for second consecutive month\")\nfor h in emp_check: print(f\" cross-check line {h[0]} [{h[1]}]: {h[3]}\")\nassert all((-1 if h[2].group(1) == \"declined\" else 1) * int(h[2].group(2).replace(\",\", \"\")) == emp for h in emp_check)\n\n# (b) assertion\nASSERT_SEC = \"Unemployment rate up for core-aged women\"\nw = find(r\"core-aged women \\(25 to 54 years old\\), the unemployment rate (rose|fell) ([\\d.]+) percentage points to ([\\d.]+)%\", ASSERT_SEC)\nm_ = find(r\"unemployment rate for core-aged men (rose|fell|increased|decreased) ([\\d.]+) percentage points to ([\\d.]+)%\", ASSERT_SEC)\nprint(\"Assertion evidence:\")\nfor h in w + m_: print(f\" line {h[0]} [{h[1]}]: {h[3]}\")\nwd, wc, wl = w[0][2].group(1), float(w[0][2].group(2)), float(w[0][2].group(3))\nmd, mc, ml = m_[0][2].group(1), float(m_[0][2].group(2)), float(m_[0][2].group(3))\nchecks = {\n \"women direction rose\": wd == \"rose\", \"women change 0.3\": wc == 0.3, \"women level 5.3\": wl == 5.3,\n \"men direction rose\": md in (\"rose\", \"increased\"), \"men change 0.2\": mc == 0.2, \"men level 5.8\": ml == 5.8,\n}\nprint(\"Element checks:\", checks)\nverdict = \"supported\" if all(checks.values()) else \"not_supported\"\nreason = (f'The release confirms the women\\'s figures (rose {wc} points to {wl}%) and the men\\'s magnitude and level, '\n f'but states that \"the unemployment rate for core-aged men {md} {mc} percentage points to {ml}%\", '\n f'contradicting the assertion that it rose.') if verdict == \"not_supported\" else \\\n \"Every element of the assertion is stated in the release.\"\n\ndata_issues = [\n {\"issue\": \"Chart, infographic, map and data tables (Tables 1-12) are not reproduced in the extract; only their headings remain (e.g. empty '## Chart 1 ' headings with trailing spaces).\",\n \"handling\": \"Only narrative text used; figures taken from sentences; trailing whitespace stripped from headings when building locators.\"},\n {\"issue\": \"The 'Sustainable Development Goals' section ends with 'the following goals:' but the list of goals is missing from the extract.\",\n \"handling\": \"Not relevant to the estimands; noted only.\"},\n {\"issue\": \"Headline figures are stated in more than one section (Highlights and a thematic section).\",\n \"handling\": \"Locator gives the Highlights section; the script cross-checks that the thematic-section values are identical (they are).\"},\n]\nif sha != EXPECTED_SHA:\n data_issues.append({\"issue\": f\"SHA-256 of extract ({sha}) differs from README\", \"handling\": \"Reported; proceeded.\"})\n\ndef wc_l(p): return p.read_bytes().count(b\"\\n\")\n\nresults = {\n \"unemployment_rate_sep2026\": ur,\n \"employment_change_sep2026_persons\": emp,\n \"locators\": {\n \"unemployment_rate_sep2026\": loc(ur_hits[0][1]),\n \"employment_change_sep2026_persons\": loc(emp_hits[0][1]),\n },\n \"assertion_verdict\": verdict,\n \"assertion_locator\": loc(ASSERT_SEC),\n \"assertion_reason\": reason,\n \"task_id\": \"F4-dev-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": data_issues,\n \"n_input_rows\": {\"README.md\": wc_l(README), f\"sources/{SRC_ID}.txt\": wc_l(SRC)},\n \"n_analysed\": 3,\n \"random_seed\": None,\n}\nPath(\"out/results.json\").write_text(json.dumps(results, indent=2, ensure_ascii=False) + \"\\n\")\nprint(json.dumps(results, indent=2, ensure_ascii=False))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis 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> out/report.md <<'EOF'\n# F4-dev-01 — Headline figures and one assertion, Statistics Canada LFS September 2026\n\n## Question\nFrom the provided text of Statistics Canada's *The Daily* release \"Labour Force Survey, September 2026\" (released 2026-10-09): (a) extract the national September 2026 unemployment rate (seasonally adjusted, %) and the monthly change in employment (seasonally adjusted, persons), each with a section locator; (b) decide whether the following assertion is supported: \"In September 2026, the unemployment rate among core-aged women (aged 25 to 54) rose 0.3 percentage points to 5.3%, and the unemployment rate among core-aged men also rose, by 0.2 percentage points, to 5.8%.\"\n\n## Data and exclusions\n- `data/sources/statcan-daily-lfs-2026-09.txt` (164 lines; SHA-256 matches the value in `data/README.md`), and `data/README.md` (9 lines). These were the only evidence used. I did not use remembered LFS values.\n- Charts, infographics, maps and Tables 1–12 are not in the extract; only their headings remain. All values below come from narrative sentences. Nothing was excluded beyond that.\n\n## Method\n`out/analysis.py` parses the extract line by line and records the most recent `##`/`###` heading for each line. Regular expressions then pull out the figures exactly as printed. The script prints every matched source line, so each value can be traced to the text:\n- Unemployment rate: Highlights, line 17, \"The unemployment rate increased 0.1 percentage points to 6.5%.\" Line 43, under \"Unemployment rate increases to 6.5%\", gives the same value (\"rose 0.1 percentage points to 6.5% in September\").\n- Employment change: Highlights, line 17, \"Employment declined by 68,000 (-0.3%) in September\". \"declined\" is coded as a negative sign. Line 24, under \"Employment falls for second consecutive month\", gives the same value.\n- Assertion: I split the assertion into six parts (direction, size of change and level, for women and for men). Each part was checked against lines 74–75 under \"Unemployment rate up for core-aged women\".\n\n## Results\n| item | value | locator |\n|---|---|---|\n| Unemployment rate, Sept 2026 | 6.5 % | statcan-daily-lfs-2026-09 \\| section \"Highlights\" |\n| Employment change, Sept 2026 | −68,000 persons | statcan-daily-lfs-2026-09 \\| section \"Highlights\" |\n| Assertion verdict | **not_supported** | statcan-daily-lfs-2026-09 \\| section \"Unemployment rate up for core-aged women\" |\n\nThe release supports the women's part of the assertion: \"the unemployment rate rose 0.3 percentage points to 5.3%\". For men, the size of the change (0.2 points) and the level (5.8%) match, but the direction does not. The release says \"the unemployment rate for core-aged men **fell** 0.2 percentage points to 5.8%\", and it explains why: employment held steady while the number of men searching for work edged down. The assertion says the rate \"also rose\", which the release directly contradicts, so the verdict is `not_supported`.\n\n## Uncertainty\nThese are point estimates copied exactly as printed; I did no estimation and no re-computation. The release notes that it treats differences as statistically significant at the 68% confidence level. The extract does not give sampling errors for these figures, so I cannot report any. The verdict rests on explicit wording (\"fell\" against \"rose\"), and the extract leaves no doubt about which is meant.\n\n## Limitations and open issues\n- The data tables are not in the extract, so the figures could not be checked against Table 1 or Table 3. The only checks possible were between sections of the text, and those agree.\n- The \"Sustainable Development Goals\" section ends with \"the following goals:\" and the list itself is missing from the extract. This does not affect any of the results.\n- Both headline figures appear in two sections. The locators name \"Highlights\", but the thematic sections given in the Method give identical values.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"The national unemployment rate in September 2026 was 6.5% (seasonally adjusted), as stated in the Highlights.\", \"results_keys\": [\"unemployment_rate_sep2026\", \"locators\"], \"status\": \"supported\", \"caveat\": \"Taken from the narrative text; the data tables are not in the extract.\"},\n {\"id\": \"C2\", \"text\": \"National employment declined by 68,000 persons in September 2026 (seasonally adjusted), as stated in the Highlights.\", \"results_keys\": [\"employment_change_sep2026_persons\", \"locators\"], \"status\": \"supported\", \"caveat\": \"Printed rounded to the nearest thousand; the sign comes from the word 'declined'.\"},\n {\"id\": \"C3\", \"text\": \"The assertion is not supported: the release says the core-aged women's unemployment rate rose 0.3 points to 5.3%, but the core-aged men's rate fell (it did not rise) 0.2 points to 5.8%.\", \"results_keys\": [\"assertion_verdict\", \"assertion_locator\", \"assertion_reason\"], \"status\": \"supported\", \"caveat\": \"The women's part, the men's size of change and the men's level all match; only the men's direction is contradicted.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\"","description":"Write report and claims files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4499,"cache_read_input_tokens":11732,"output_tokens":1945,"output_tokens_details":{"thinking_tokens":36},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4499},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXjk3172w7QGRBsUpFME","requestRef":"ac0f0e04-d0f9-4607-b2a7-33dcb7a0374d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4fba95da-2f91-4d27-bddc-a66de6f239ed","timestamp":"2026-10-30T03:48:29.306Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0001","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"4fba95da-2f91-4d27-bddc-a66de6f239ed","isSidechain":false,"promptId":"3f4ad405-5b99-40b2-b251-59f460202cf7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XHkj1cyF7vx7WnMWovKc9E","type":"tool_result","content":"ok","is_error":false}]},"uuid":"8d03c62a-2068-404f-89b5-bdd24fea2288","timestamp":"2026-10-30T03:48:29.373Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4fba95da-2f91-4d27-bddc-a66de6f239ed","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0001","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","hash":"a075e071483382a5bea288761052b0967a662d86926bf4710c9529b7c6574ef2","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01XHkj1cyF7vx7WnMWovKc9E","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-dev-01 — Headline figures and one assertion, Statistics Canada LFS September 2026\n\n## Question\nFrom the provided text of Statistics Canada's *The Daily* release \"Labour Force Survey, September 2026\" (released 2026-10-09): (a) extract the national September 2026 unemployment rate (seasonally adjusted, %) and the monthly change in employment (seasonally adjusted, persons), each with a section locator; (b) decide whether the following assertion is supported: \"In September 2026, the unemployment rate among core-aged women (aged 25 to 54) rose 0.3 percentage points to 5.3%, and the unemployment rate among core-aged men also rose, by 0.2 percentage points, to 5.8%.\"\n\n## Data and exclusions\n- `data/sources/statcan-daily-lfs-2026-09.txt` (164 lines; SHA-256 matches the value in `data/README.md`), and `data/README.md` (9 lines). These were the only evidence used. I did not use remembered LFS values.\n- Charts, infographics, maps and Tables 1–12 are not in the extract; only their headings remain. All values below come from narrative sentences. Nothing was excluded beyond that.\n\n## Method\n`out/analysis.py` parses the extract line by line and records the most recent `##`/`###` heading for each line. Regular expressions then pull out the figures exactly as printed. The script prints every matched source line, so each value can be traced to the text:\n- Unemployment rate: Highlights, line 17, \"The unemployment rate increased 0.1 percentage points to 6.5%.\" Line 43, under \"Unemployment rate increases to 6.5%\", gives the same value (\"rose 0.1 percentage points to 6.5% in September\").\n- Employment change: Highlights, line 17, \"Employment declined by 68,000 (-0.3%) in September\". \"declined\" is coded as a negative sign. Line 24, under \"Employment falls for second consecutive month\", gives the same value.\n- Assertion: I split the assertion into six parts (direction, size of change and level, for women and for men). Each part was checked against lines 74–75 under \"Unemployment rate up for core-aged women\".\n\n## Results\n| item | value | locator |\n|---|---|---|\n| Unemployment rate, Sept 2026 | 6.5 % | statcan-daily-lfs-2026-09 \\| section \"Highlights\" |\n| Employment change, Sept 2026 | −68,000 persons | statcan-daily-lfs-2026-09 \\| section \"Highlights\" |\n| Assertion verdict | **not_supported** | statcan-daily-lfs-2026-09 \\| section \"Unemployment rate up for core-aged women\" |\n\nThe release supports the women's part of the assertion: \"the unemployment rate rose 0.3 percentage points to 5.3%\". For men, the size of the change (0.2 points) and the level (5.8%) match, but the direction does not. The release says \"the unemployment rate for core-aged men **fell** 0.2 percentage points to 5.8%\", and it explains why: employment held steady while the number of men searching for work edged down. The assertion says the rate \"also rose\", which the release directly contradicts, so the verdict is `not_supported`.\n\n## Uncertainty\nThese are point estimates copied exactly as printed; I did no estimation and no re-computation. The release notes that it treats differences as statistically significant at the 68% confidence level. The extract does not give sampling errors for these figures, so I cannot report any. The verdict rests on explicit wording (\"fell\" against \"rose\"), and the extract leaves no doubt about which is meant.\n\n## Limitations and open issues\n- The data tables are not in the extract, so the figures could not be checked against Table 1 or Table 3. The only checks possible were between sections of the text, and those agree.\n- The \"Sustainable Development Goals\" section ends with \"the following goals:\" and the list itself is missing from the extract. This does not affect any of the results.\n- Both headline figures appear in two sections. The locators name \"Highlights\", but the thematic sections given in the Method give identical values.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"The national unemployment rate in September 2026 was 6.5% (seasonally adjusted), as stated in the Highlights.\", \"results_keys\": [\"unemployment_rate_sep2026\", \"locators\"], \"status\": \"supported\", \"caveat\": \"Taken from the narrative text; the data tables are not in the extract.\"},\n {\"id\": \"C2\", \"text\": \"National employment declined by 68,000 persons in September 2026 (seasonally adjusted), as stated in the Highlights.\", \"results_keys\": [\"employment_change_sep2026_persons\", \"locators\"], \"status\": \"supported\", \"caveat\": \"Printed rounded to the nearest thousand; the sign comes from the word 'declined'.\"},\n {\"id\": \"C3\", \"text\": \"The assertion is not supported: the release says the core-aged women's unemployment rate rose 0.3 points to 5.3%, but the core-aged men's rate fell (it did not rise) 0.2 points to 5.8%.\", \"results_keys\": [\"assertion_verdict\", \"assertion_locator\", \"assertion_reason\"], \"status\": \"supported\", \"caveat\": \"The women's part, the men's size of change and the men's level all match; only the men's direction is contradicted.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\"","description":"Write report and claims files"}}]}}39{"type":"api-request","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","id":"b5d37d2f-80ec-4c3e-80a6-b163db7cb1e6","timestamp":"2026-10-30T03:48:29.378Z","version":"2.1.314","querySource":"sdk","shapeHash":"a9e7dfc67201f8244ca5c606e0d408b74c6b31c6f4d0becb7dd0545da0346c33","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"ac0f0e04-d0f9-4607-b2a7-33dcb7a0374d","keep":10,"tail":["a075e071483382a5bea288761052b0967a662d86926bf4710c9529b7c6574ef2","c4cba29d8396d4ac5430b2fb8b19cd58c9d0761ea7790a249c2b59ef1e5b76b5"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"527169eef45785c9b232ef478c6ffdbf05879b138449ace95d9af08c5a000823"}40{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"8d03c62a-2068-404f-89b5-bdd24fea2288","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea"}41{"type":"atis-latch","atis":"","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea"}42{"parentUuid":"8d03c62a-2068-404f-89b5-bdd24fea2288","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXjmGSGg2e8MtU7Q64tp","type":"message","role":"assistant","content":[{"type":"text","text":"I've extracted both headline figures from the release text, and the assertion about core-aged unemployment is **not supported**. The analysis script ran and regenerated `out/results.json`; it prints each matched source line, and its consistency checks passed.\n\n| Item | Result | Where it's stated |\n|---|---|---|\n| September 2026 unemployment rate | 6.5% | \"Highlights\" (same value under \"Unemployment rate increases to 6.5%\") |\n| Change in employment, September 2026 | −68,000 persons (\"declined by 68,000\") | \"Highlights\" (same value under \"Employment falls for second consecutive month\") |\n| Assertion verdict | not supported | \"Unemployment rate up for core-aged women\" |\n\n**Why the assertion fails:** the women's part is correct: the release says their unemployment rate \"rose 0.3 percentage points to 5.3%\". For men, the size of the change (0.2 points) and the level (5.8%) also match. But the release says the men's rate \"**fell** 0.2 percentage points to 5.8%\", while the assertion says it rose.\n\n**Limitations:**\n- The extract leaves out the data tables, so I could only cross-check the figures between sections of the text, and those agree.\n- The extract gives no sampling errors, so there's no uncertainty range around these numbers.\n- The list of goals in the \"Sustainable Development Goals\" section is cut off in the extract. This doesn't affect any of the results.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1952,"cache_read_input_tokens":16231,"output_tokens":511,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1952},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXjmGAPkMoCL3Ff86NCV","requestRef":"b5d37d2f-80ec-4c3e-80a6-b163db7cb1e6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f8f35afb-0774-43fc-88d3-f4209bcb9837","timestamp":"2026-10-30T03:48:34.200Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0001","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","version":"2.1.314","gitBranch":"HEAD"}43{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"f8f35afb-0774-43fc-88d3-f4209bcb9837","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea"}44{"type":"cost-state","sessionId":"e87c6a93-b2a9-49ef-9e9c-af14a9a542ea","totalCostUSD":0.214208,"totalAPIDuration":53045,"totalAPIDurationWithoutRetries":53041,"totalToolDuration":292,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":53542,"startTime":1793332060664,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":5764,"thinkingTokens":404,"cacheReadInputTokens":39785,"cacheCreationInputTokens":18183,"cacheCreationEphemeral5mInputTokens":18183,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.214208}},"hasUnknownModelCost":false}45