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The estimands are the **balanced-panel means**: for each year, the simple (unweighted) mean of life expectancy over the countries that have a non-missing value in every year from 2000 to 2022 inclusive, each country counted once; the main quantities are the balanced means for 2000 and 2022 and their difference, together with the composition of the balanced set. A naive mean over whatever countries happen to be available in a year is not the estimand, but it is requested as a descriptive baseline for 2022.\n\n## Inputs\n- `data/life_expectancy_panel.csv` — observed data: life expectancy at birth, total (years), World Bank World Development Indicators indicator `SP.DYN.LE00.IN`, 20 countries, years 2000–2022. Columns `country_name` (string), `iso3` (ISO alpha-3 code), `year` (integer), `life_expectancy` (years). Source URL, retrieval date, license and SHA-256 are in `data/README.md`. The file was assembled from two extractions of the API response and may contain assembly errors; an empty cell means no value is recorded for that country-year.\n\n## Information-availability rules\n- Use only `data/life_expectancy_panel.csv`. Do not impute, interpolate or look up missing values: a country-year without a recorded value is missing.\n- A country is identified by its `iso3` code; each country contributes at most one observation per year.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-02\" |\n| `n_countries_total` | integer | countries | distinct countries (iso3) present in the file |\n| `n_countries_balanced` | integer | countries | countries with a recorded value in every year 2000–2022 |\n| `n_countries_incomplete` | integer | countries | distinct countries not in the balanced set |\n| `balanced_countries` | array of strings | — | iso3 codes of the balanced set, sorted |\n| `n_analysed` | integer | country-years | rows of the balanced analytic dataset (countries × years) |\n| `mean_2000_balanced` | number | years | balanced-panel mean life expectancy in 2000 |\n| `mean_2022_balanced` | number | years | balanced-panel mean life expectancy in 2022 |\n| `change_2000_2022_balanced` | number | years | `mean_2022_balanced − mean_2000_balanced` |\n| `mean_2022_all_available` | number | years | baseline: mean over all distinct countries with a recorded 2022 value, each counted once |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"life_expectancy_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the balanced analytic dataset: one row per country-year of the balanced set, columns `iso3`, `country_name`, `year`, `life_expectancy`; exactly `n_analysed` rows, one row per (iso3, year) pair.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\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`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\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). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\ntotal 80\ndrwxr-xr-x 11 simon-pierreboucher staff 352 oct. 30 00:43 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:42 ..\n-rw-r--r-- 1 simon-pierreboucher staff 5895 oct. 30 00:42 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 8620 oct. 30 00:42 analytic.csv\n-rw-r--r-- 1 simon-pierreboucher staff 340 oct. 30 00:42 balanced_means_by_year.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1219 oct. 30 00:43 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 571 oct. 30 00:42 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 3607 oct. 30 00:43 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 2516 oct. 30 00:42 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:43 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 00:43 state\n{\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": 20,\n \"n_countries_balanced\": 15,\n \"n_countries_incomplete\": 5,\n \"balanced_countries\": [\n \"AUS\",\n \"AUT\",\n \"BEL\",\n \"CAN\",\n \"DEU\",\n \"ESP\",\n \"FRA\",\n \"GBR\",\n \"ITA\",\n \"JPN\",\n \"KOR\",\n \"NLD\",\n \"SWE\",\n \"TUR\",\n \"USA\"\n ],\n \"incomplete_countries\": [\n \"BGD\",\n \"CHE\",\n \"ETH\",\n \"IND\",\n \"NGA\"\n ],\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692,\n \"n_countries_2022_available\": 20,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"TUR: 23 duplicated (iso3, year) rows from the two extractions, under country names ['Turkey', 'Turkiye']; values identical in every duplicated year\",\n \"handling\": \"Kept one row per (iso3, year) (first occurrence); country identified by iso3; removed rows logged in exclusions.csv\"\n },\n {\n \"issue\": \"TUR: multiple country_name spellings ['Turkey', 'Turkiye']\",\n \"handling\": \"Identified by iso3; analytic.csv uses 'Turkiye' (last-listed name)\"\n },\n {\n \"issue\": \"CHE 2007: empty life_expectancy cell\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"BGD: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n },\n {\n \"issue\": \"ETH: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n },\n {\n \"issue\": \"IND: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n },\n {\n \"issue\": \"NGA: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}[\n {\"id\":\"C1\",\"text\":\"15 of 20 countries have a recorded value in every year 2000-2022; 345 country-years analysed.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Counts after collapsing duplicated TUR rows.\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted; balanced set is high/upper-middle income only.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries is 78.692 years, lower than the balanced 2022 mean because lower-life-expectancy countries are excluded from the balanced set.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only.\"},\n {\"id\":\"C4\",\"text\":\"Data issues: TUR duplicated under two names with identical values (deduplicated), CHE 2007 missing, BGD/ETH/IND/NGA lacking 2000-2010.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"No imputation applied.\"}\n]","is_error":false}]},"uuid":"dc64aa2d-adcb-4110-adcf-9b4d0509a6ee","timestamp":"2026-10-30T04:43:50.688Z","toolUseResult":{"stdout":"# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Scientific question\nMeasure the change in life expectancy at birth between 2000 and 2022 across a fixed set of countries. The estimands are the **balanced-panel means**: for each year, the simple (unweighted) mean of life expectancy over the countries that have a non-missing value in every year from 2000 to 2022 inclusive, each country counted once; the main quantities are the balanced means for 2000 and 2022 and their difference, together with the composition of the balanced set. A naive mean over whatever countries happen to be available in a year is not the estimand, but it is requested as a descriptive baseline for 2022.\n\n## Inputs\n- `data/life_expectancy_panel.csv` — observed data: life expectancy at birth, total (years), World Bank World Development Indicators indicator `SP.DYN.LE00.IN`, 20 countries, years 2000–2022. Columns `country_name` (string), `iso3` (ISO alpha-3 code), `year` (integer), `life_expectancy` (years). Source URL, retrieval date, license and SHA-256 are in `data/README.md`. The file was assembled from two extractions of the API response and may contain assembly errors; an empty cell means no value is recorded for that country-year.\n\n## Information-availability rules\n- Use only `data/life_expectancy_panel.csv`. Do not impute, interpolate or look up missing values: a country-year without a recorded value is missing.\n- A country is identified by its `iso3` code; each country contributes at most one observation per year.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-02\" |\n| `n_countries_total` | integer | countries | distinct countries (iso3) present in the file |\n| `n_countries_balanced` | integer | countries | countries with a recorded value in every year 2000–2022 |\n| `n_countries_incomplete` | integer | countries | distinct countries not in the balanced set |\n| `balanced_countries` | array of strings | — | iso3 codes of the balanced set, sorted |\n| `n_analysed` | integer | country-years | rows of the balanced analytic dataset (countries × years) |\n| `mean_2000_balanced` | number | years | balanced-panel mean life expectancy in 2000 |\n| `mean_2022_balanced` | number | years | balanced-panel mean life expectancy in 2022 |\n| `change_2000_2022_balanced` | number | years | `mean_2022_balanced − mean_2000_balanced` |\n| `mean_2022_all_available` | number | years | baseline: mean over all distinct countries with a recorded 2022 value, each counted once |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"life_expectancy_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the balanced analytic dataset: one row per country-year of the balanced set, columns `iso3`, `country_name`, `year`, `life_expectancy`; exactly `n_analysed` rows, one row per (iso3, year) pair.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\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`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\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). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\ntotal 80\ndrwxr-xr-x 11 simon-pierreboucher staff 352 oct. 30 00:43 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:42 ..\n-rw-r--r-- 1 simon-pierreboucher staff 5895 oct. 30 00:42 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 8620 oct. 30 00:42 analytic.csv\n-rw-r--r-- 1 simon-pierreboucher staff 340 oct. 30 00:42 balanced_means_by_year.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1219 oct. 30 00:43 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 571 oct. 30 00:42 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 3607 oct. 30 00:43 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 2516 oct. 30 00:42 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:43 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 00:43 state\n{\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": 20,\n \"n_countries_balanced\": 15,\n \"n_countries_incomplete\": 5,\n \"balanced_countries\": [\n \"AUS\",\n \"AUT\",\n \"BEL\",\n \"CAN\",\n \"DEU\",\n \"ESP\",\n \"FRA\",\n \"GBR\",\n \"ITA\",\n \"JPN\",\n \"KOR\",\n \"NLD\",\n \"SWE\",\n \"TUR\",\n \"USA\"\n ],\n \"incomplete_countries\": [\n \"BGD\",\n \"CHE\",\n \"ETH\",\n \"IND\",\n \"NGA\"\n ],\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692,\n \"n_countries_2022_available\": 20,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"TUR: 23 duplicated (iso3, year) rows from the two extractions, under country names ['Turkey', 'Turkiye']; values identical in every duplicated year\",\n \"handling\": \"Kept one row per (iso3, year) (first occurrence); country identified by iso3; removed rows logged in exclusions.csv\"\n },\n {\n \"issue\": \"TUR: multiple country_name spellings ['Turkey', 'Turkiye']\",\n \"handling\": \"Identified by iso3; analytic.csv uses 'Turkiye' (last-listed name)\"\n },\n {\n \"issue\": \"CHE 2007: empty life_expectancy cell\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"BGD: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n },\n {\n \"issue\": \"ETH: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n },\n {\n \"issue\": \"IND: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n },\n {\n \"issue\": \"NGA: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}[\n {\"id\":\"C1\",\"text\":\"15 of 20 countries have a recorded value in every year 2000-2022; 345 country-years analysed.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Counts after collapsing duplicated TUR rows.\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted; balanced set is high/upper-middle income only.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries is 78.692 years, lower than the balanced 2022 mean because lower-life-expectancy countries are excluded from the balanced set.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only.\"},\n {\"id\":\"C4\",\"text\":\"Data issues: TUR duplicated under two names with identical values (deduplicated), CHE 2007 missing, BGD/ETH/IND/NGA lacking 2000-2010.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"No imputation applied.\"}\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"90ec188e-1492-4e4b-a4ea-11aa8918a468","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030","hash":"eaea25ef21bc0957dfb556e520e8a15e8be9ebd3c0bd6d79d3d3bc0170cea577","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSlQUKEAgTGAI4AUIIdGhpbmtpbmcSDK0P7En9mz63vFeOCxoMPEnfvhgC/WkaRcgNIjCiOyyD6S/MM99WFzAECVHzPIhh8nG4NlXZIfLYV31eTQkkCPZ1u69AtldJ4Mo4FDAqsgQBwNgirnz5T6CWTRR7Aoz1YNh46rbpRxUSPLb21awp/7lisz4iXCzArxLj3mwfrYRnqfFpKiJC5vO9HCDtWGp7B7CHoAJOdQNqSKr9BI0b+Iek0jlfFXWKFi9KpZ4U/VNjY9C6RFPp/QXSYa0VCyAh26AYWmwk2WLrFQ7fGP7jeCWMtp/nSpKMgrTF+ps+4pCTZ22AZ6zP1ZYH4srSYfsl+IcUfh0FznYC+B16mU2ToiKGlTYDISfDWhMxoXrC5aVAGMr01H47hayqMrlM6/qLImxfZmhloNvhCFA7L2dRaKWW3pPCKnqsnM4STyyI4L5Y3g188t7DKQqIhbHxD04JHGiSPTYNs/vGGXakD26Tq54peYhb7FVIT2/n8Nfmg5sjKDYz2ncFkTOnIETvYuhmaj04ymk9TVUDJylLInxDYITXxdE5bB9ujUAzYDXbuKrWNPMhbI47o3HPTwFyhT6Tor7J62Q33xuinXYO9Cj8UNO+7RY13o4IKsaEfu/2nLNVSDlqsDX6TddL7Jcsoz5UaRf8v0pQLIHrs5d7MBzkaXsoK3uGVPNCvzeP8AyO5loiE7mD0UuM22gXMy09Tpp+4i1/Rq3zOgCsN2Bizn6wvf4t2S6aNH+ySnE8f0zGznBsB+vh7vcL6i3k/tS+Jzm5eqkS+cMebKDyOu/yH+Jk6VmMPT+Y79Q80WqBxrnUPJ3QMqVtJDtgYGUQqCddJKEgPiyLjnp+tZcicC5aTKSWf+epGAE="},{"type":"tool_use","id":"toolu_01EUz3ghA1YDpaCJHXcuwdtb","name":"Bash","input":{"command":"cat TASK.md; 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Deterministic; no randomness used.\"\"\"\nimport json, os\nimport pandas as pd\n\nIN = \"data/life_expectancy_panel.csv\"\nOUT = \"out\"\nYEARS = list(range(2000, 2023))\nos.makedirs(OUT, exist_ok=True)\n\nraw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str}, keep_default_na=True)\nn_raw = len(raw)\nissues, excl = [], []\n\ndef step(name, rule, before, after, why):\n excl.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\ndf = raw.copy()\ndf[\"iso3\"] = df[\"iso3\"].str.strip()\ndf[\"year\"] = df[\"year\"].astype(int)\n\n# 1. Exact/conflicting duplicates of (iso3, year)\ndup = df[df.duplicated([\"iso3\", \"year\"], keep=False)]\nif len(dup):\n g = dup.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False)\n conflicting = g[g > 1]\n if len(conflicting):\n raise SystemExit(f\"Conflicting duplicate values: {conflicting}\")\n for iso, sub in dup.groupby(\"iso3\"):\n names = sorted(sub.country_name.unique())\n issues.append({\n \"issue\": f\"{iso}: {sub.duplicated(['iso3','year']).sum()} duplicated (iso3, year) rows \"\n f\"from the two extractions, under country names {names}; values identical in every duplicated year\",\n \"handling\": \"Kept one row per (iso3, year) (first occurrence); country identified by iso3; \"\n \"removed rows logged in exclusions.csv\"})\nb = len(df)\ndf = df.drop_duplicates([\"iso3\", \"year\"], keep=\"first\")\nstep(\"1_dedupe\", \"drop duplicate (iso3, year) rows with identical values\", b, len(df),\n \"each country contributes at most one observation per year (TASK rule); duplicates are assembly artefacts\")\n\n# harmonise country name per iso3 (use most recent name in file for display)\nname_map = {}\nfor iso, sub in raw.groupby(\"iso3\"):\n names = list(dict.fromkeys(sub.country_name))\n name_map[iso.strip()] = names[-1]\n if len(names) > 1:\n issues.append({\"issue\": f\"{iso}: multiple country_name spellings {names}\",\n \"handling\": f\"Identified by iso3; analytic.csv uses '{names[-1]}' (last-listed name)\"})\ndf[\"country_name\"] = df[\"iso3\"].map(name_map)\n\n# out-of-window years\nb = len(df)\ndf = df[df.year.between(2000, 2022)]\nstep(\"2_year_window\", \"keep years 2000-2022\", b, len(df), \"estimand window\")\n\n# 3. Missing values\nmiss = df[df.life_expectancy.isna()]\nfor _, r in miss.iterrows():\n issues.append({\"issue\": f\"{r.iso3} {r.year}: empty life_expectancy cell\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set\"})\nb = len(df)\ndf = df[df.life_expectancy.notna()]\nstep(\"3_drop_missing\", \"drop rows with empty life_expectancy\", b, len(df),\n \"missing values are not imputed (TASK rule)\")\n\n# rows absent for countries\nfor iso, sub in df.groupby(\"iso3\"):\n present = set(sub.year)\n absent = [y for y in YEARS if y not in present and not ((miss.iso3 == iso) & (miss.year == y)).any()]\n if absent:\n issues.append({\"issue\": f\"{iso}: no rows at all for years {min(absent)}-{max(absent)} ({len(absent)} years); \"\n f\"likely from the narrower earlier extraction\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; \"\n \"its 2022 value still enters the all-available baseline\"})\n\n# plausibility\nif not df.life_expectancy.between(20, 100).all():\n raise SystemExit(\"implausible values\")\n\nall_countries = sorted(raw.iso3.str.strip().unique())\ncounts = df.groupby(\"iso3\").year.nunique()\nbalanced = sorted(counts[counts == len(YEARS)].index)\nincomplete = sorted(set(all_countries) - set(balanced))\n\nb = len(df)\nana = df[df.iso3.isin(balanced)].sort_values([\"iso3\", \"year\"])\nstep(\"4_balanced\", \"keep countries with a recorded value in every year 2000-2022\", b, len(ana),\n f\"balanced-panel estimand; excluded incomplete countries {incomplete}\")\nana = ana[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert len(ana) == len(balanced) * len(YEARS)\nassert not ana.duplicated([\"iso3\", \"year\"]).any()\nana.to_csv(f\"{OUT}/analytic.csv\", index=False)\n\nex = pd.DataFrame(excl)\nassert ex.n_before.iloc[0] == n_raw and ex.n_after.iloc[-1] == len(ana)\nex.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm00 = float(ana[ana.year == 2000].life_expectancy.mean())\nm22 = float(ana[ana.year == 2022].life_expectancy.mean())\navail22 = df[df.year == 2022].drop_duplicates(\"iso3\")\nm22_all = float(avail22.life_expectancy.mean())\n\n# per-year balanced means (supplementary, not required)\nper_year = ana.groupby(\"year\").life_expectancy.mean().round(6)\nper_year.to_csv(f\"{OUT}/balanced_means_by_year.csv\")\n\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": len(all_countries),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"incomplete_countries\": incomplete,\n \"n_analysed\": int(len(ana)),\n \"mean_2000_balanced\": round(m00, 6),\n \"mean_2022_balanced\": round(m22, 6),\n \"change_2000_2022_balanced\": round(m22 - m00, 6),\n \"mean_2022_all_available\": round(m22_all, 6),\n \"n_countries_2022_available\": int(len(avail22)),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_countries_balanced\", \"n_analysed\", \"mean_2000_balanced\",\n \"mean_2022_balanced\", \"change_2000_2022_balanced\",\n \"mean_2022_all_available\"]}, indent=1))\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedupe,\"drop duplicate (iso3, year) rows with identical values\",439,416,23,each country contributes at most one observation per year (TASK rule); duplicates are assembly artefacts\n2_year_window,keep years 2000-2022,416,416,0,estimand window\n3_drop_missing,drop rows with empty life_expectancy,416,415,1,missing values are not imputed (TASK rule)\n4_balanced,keep countries with a recorded value in every year 2000-2022,415,345,70,\"balanced-panel estimand; excluded incomplete countries ['BGD', 'CHE', 'ETH', 'IND', 'NGA']\"\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow much did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change from 2000 to 2022 in a fixed set of countries? The estimand is the unweighted mean over countries (by iso3) with a recorded value in every year 2000–2022, each counted once.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct countries (`n_countries_total`). Problems found (`data_issues`):\n- Türkiye (TUR) appears twice for every year, under the names \"Turkey\" and \"Turkiye\". The two copies have identical values. I kept one row per (iso3, year), which removed 23 rows.\n- Switzerland (CHE) has an empty cell for 2007. I treated it as missing and did not impute it, so CHE is not in the balanced set.\n- Bangladesh, Ethiopia, India and Nigeria (BGD, ETH, IND, NGA) have no rows for 2000–2010. They are not in the balanced set.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows → 416 after removing duplicates → 416 after the year-window filter → 415 after dropping the missing cell → 345 after keeping only balanced countries (`n_analysed`). The balanced set has 15 countries (`n_countries_balanced`, `balanced_countries`: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA). 5 countries are incomplete (`n_countries_incomplete`).\n\n## Method\nFor each year I took the simple mean over the 15 balanced countries (`out/analysis.py`; `out/balanced_means_by_year.csv` is a supplementary output). The change is the 2022 mean minus the 2000 mean. The baseline is the mean over all distinct countries with a recorded 2022 value (20 countries, `n_countries_2022_available`), with TUR counted once. The method uses no randomness (`random_seed` = null).\n\n## Results\n- Balanced mean in 2000: **78.060 years** (`mean_2000_balanced` = 78.060267).\n- Balanced mean in 2022: **81.553 years** (`mean_2022_balanced` = 81.553067).\n- Change from 2000 to 2022: **+3.493 years** (`change_2000_2022_balanced` = 3.4928).\n- Baseline, mean over all 20 countries with a 2022 value: 78.692 years (`mean_2022_all_available`). This is lower than the balanced 2022 mean because four of the five excluded countries (BGD, ETH, IND, NGA) have lower life expectancy. The baseline is not a measure of change over time.\n- Feasibility: `feasibility` = \"complete\".\n\n## Uncertainty\nThese are descriptive means over a fixed set of countries; they are not estimates from a sample. I therefore report no sampling standard errors or confidence intervals. The remaining uncertainty is measurement and revision error in the WDI figures, which I cannot quantify from this file. I checked the figures independently with a pivot/dropna recomputation, which matched exactly, and a re-run of the script gave byte-identical output (`out/state/checks.md`).\n\n## Limitations and open issues\n- The balanced set contains only high- and upper-middle-income countries (all OECD members). Switzerland is excluded because of a single missing year (2007), and four lower-income countries are excluded because they have no data before 2011. The result therefore does not represent all 20 countries.\n- The countries are equally weighted, not weighted by population.\n- I resolved the TUR duplicates only because both copies agree exactly. The script stops if a duplicate pair ever has conflicting values.\n- Year-on-year jumps of more than 1.5 years occur in 2020–2022 (e.g. ESP, USA, IND, TUR, BGD). I kept them unchanged, since the task allows no external lookup. They are consistent with COVID-era mortality shocks and recovery.\n# Data provenance — F1-conf-02\n\n`life_expectancy_panel.csv` — observed data (not simulated): **Life expectancy at birth, total (years)**, indicator `SP.DYN.LE00.IN`, World Development Indicators (World Bank, source id 2, release last updated 2026-10-08), for 20 countries (Australia, Austria, Bangladesh, Belgium, Canada, Ethiopia, France, Germany, India, Italy, Japan, Korea Rep., Netherlands, Nigeria, Spain, Sweden, Switzerland, Türkiye, United Kingdom, United States), years 2000–2022.\n\n- Source URL (JSON API): `https://api.worldbank.org/v2/country/CAN;USA;GBR;FRA;DEU;ITA;ESP;JPN;KOR;AUS;SWE;NLD;CHE;BEL;AUT;TUR;IND;NGA;ETH;BGD/indicator/SP.DYN.LE00.IN?format=json&per_page=2000&date=2000:2022`\n- Retrieved: 2026-10-30T03:25Z. SHA-256 of the raw API response: `ead88ee94d5b6cc6f8a055c9f2ec79f232c37faf8baca0ea122dbfc0550ff27e`\n- License: World Bank open data, Creative Commons Attribution 4.0 (CC BY-4.0).\n- Columns: `country_name` (string), `iso3` (ISO-3166 alpha-3 code as returned by the API), `year` (integer), `life_expectancy` (years, 3 decimals). Values were rounded to 3 decimals; nothing else was changed in the values.\n- The panel was assembled by the task author from two extractions of the API response (one earlier, narrower extraction and one later, wider one) and may contain assembly errors. An empty `life_expectancy` cell means no value was recorded for that country-year in the file.\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634","is_error":false}]},"uuid":"26dadbf4-6347-4ed6-890a-f4f61755b6b6","timestamp":"2026-10-30T04:43:52.526Z","toolUseResult":{"stdout":"\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/life_expectancy_panel.csv. Deterministic; no randomness used.\"\"\"\nimport json, os\nimport pandas as pd\n\nIN = \"data/life_expectancy_panel.csv\"\nOUT = \"out\"\nYEARS = list(range(2000, 2023))\nos.makedirs(OUT, exist_ok=True)\n\nraw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str}, keep_default_na=True)\nn_raw = len(raw)\nissues, excl = [], []\n\ndef step(name, rule, before, after, why):\n excl.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\ndf = raw.copy()\ndf[\"iso3\"] = df[\"iso3\"].str.strip()\ndf[\"year\"] = df[\"year\"].astype(int)\n\n# 1. Exact/conflicting duplicates of (iso3, year)\ndup = df[df.duplicated([\"iso3\", \"year\"], keep=False)]\nif len(dup):\n g = dup.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False)\n conflicting = g[g > 1]\n if len(conflicting):\n raise SystemExit(f\"Conflicting duplicate values: {conflicting}\")\n for iso, sub in dup.groupby(\"iso3\"):\n names = sorted(sub.country_name.unique())\n issues.append({\n \"issue\": f\"{iso}: {sub.duplicated(['iso3','year']).sum()} duplicated (iso3, year) rows \"\n f\"from the two extractions, under country names {names}; values identical in every duplicated year\",\n \"handling\": \"Kept one row per (iso3, year) (first occurrence); country identified by iso3; \"\n \"removed rows logged in exclusions.csv\"})\nb = len(df)\ndf = df.drop_duplicates([\"iso3\", \"year\"], keep=\"first\")\nstep(\"1_dedupe\", \"drop duplicate (iso3, year) rows with identical values\", b, len(df),\n \"each country contributes at most one observation per year (TASK rule); duplicates are assembly artefacts\")\n\n# harmonise country name per iso3 (use most recent name in file for display)\nname_map = {}\nfor iso, sub in raw.groupby(\"iso3\"):\n names = list(dict.fromkeys(sub.country_name))\n name_map[iso.strip()] = names[-1]\n if len(names) > 1:\n issues.append({\"issue\": f\"{iso}: multiple country_name spellings {names}\",\n \"handling\": f\"Identified by iso3; analytic.csv uses '{names[-1]}' (last-listed name)\"})\ndf[\"country_name\"] = df[\"iso3\"].map(name_map)\n\n# out-of-window years\nb = len(df)\ndf = df[df.year.between(2000, 2022)]\nstep(\"2_year_window\", \"keep years 2000-2022\", b, len(df), \"estimand window\")\n\n# 3. Missing values\nmiss = df[df.life_expectancy.isna()]\nfor _, r in miss.iterrows():\n issues.append({\"issue\": f\"{r.iso3} {r.year}: empty life_expectancy cell\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set\"})\nb = len(df)\ndf = df[df.life_expectancy.notna()]\nstep(\"3_drop_missing\", \"drop rows with empty life_expectancy\", b, len(df),\n \"missing values are not imputed (TASK rule)\")\n\n# rows absent for countries\nfor iso, sub in df.groupby(\"iso3\"):\n present = set(sub.year)\n absent = [y for y in YEARS if y not in present and not ((miss.iso3 == iso) & (miss.year == y)).any()]\n if absent:\n issues.append({\"issue\": f\"{iso}: no rows at all for years {min(absent)}-{max(absent)} ({len(absent)} years); \"\n f\"likely from the narrower earlier extraction\",\n \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; \"\n \"its 2022 value still enters the all-available baseline\"})\n\n# plausibility\nif not df.life_expectancy.between(20, 100).all():\n raise SystemExit(\"implausible values\")\n\nall_countries = sorted(raw.iso3.str.strip().unique())\ncounts = df.groupby(\"iso3\").year.nunique()\nbalanced = sorted(counts[counts == len(YEARS)].index)\nincomplete = sorted(set(all_countries) - set(balanced))\n\nb = len(df)\nana = df[df.iso3.isin(balanced)].sort_values([\"iso3\", \"year\"])\nstep(\"4_balanced\", \"keep countries with a recorded value in every year 2000-2022\", b, len(ana),\n f\"balanced-panel estimand; excluded incomplete countries {incomplete}\")\nana = ana[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert len(ana) == len(balanced) * len(YEARS)\nassert not ana.duplicated([\"iso3\", \"year\"]).any()\nana.to_csv(f\"{OUT}/analytic.csv\", index=False)\n\nex = pd.DataFrame(excl)\nassert ex.n_before.iloc[0] == n_raw and ex.n_after.iloc[-1] == len(ana)\nex.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm00 = float(ana[ana.year == 2000].life_expectancy.mean())\nm22 = float(ana[ana.year == 2022].life_expectancy.mean())\navail22 = df[df.year == 2022].drop_duplicates(\"iso3\")\nm22_all = float(avail22.life_expectancy.mean())\n\n# per-year balanced means (supplementary, not required)\nper_year = ana.groupby(\"year\").life_expectancy.mean().round(6)\nper_year.to_csv(f\"{OUT}/balanced_means_by_year.csv\")\n\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": len(all_countries),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"incomplete_countries\": incomplete,\n \"n_analysed\": int(len(ana)),\n \"mean_2000_balanced\": round(m00, 6),\n \"mean_2022_balanced\": round(m22, 6),\n \"change_2000_2022_balanced\": round(m22 - m00, 6),\n \"mean_2022_all_available\": round(m22_all, 6),\n \"n_countries_2022_available\": int(len(avail22)),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_countries_balanced\", \"n_analysed\", \"mean_2000_balanced\",\n \"mean_2022_balanced\", \"change_2000_2022_balanced\",\n \"mean_2022_all_available\"]}, indent=1))\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedupe,\"drop duplicate (iso3, year) rows with identical values\",439,416,23,each country contributes at most one observation per year (TASK rule); duplicates are assembly artefacts\n2_year_window,keep years 2000-2022,416,416,0,estimand window\n3_drop_missing,drop rows with empty life_expectancy,416,415,1,missing values are not imputed (TASK rule)\n4_balanced,keep countries with a recorded value in every year 2000-2022,415,345,70,\"balanced-panel estimand; excluded incomplete countries ['BGD', 'CHE', 'ETH', 'IND', 'NGA']\"\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow much did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change from 2000 to 2022 in a fixed set of countries? The estimand is the unweighted mean over countries (by iso3) with a recorded value in every year 2000–2022, each counted once.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct countries (`n_countries_total`). Problems found (`data_issues`):\n- Türkiye (TUR) appears twice for every year, under the names \"Turkey\" and \"Turkiye\". The two copies have identical values. I kept one row per (iso3, year), which removed 23 rows.\n- Switzerland (CHE) has an empty cell for 2007. I treated it as missing and did not impute it, so CHE is not in the balanced set.\n- Bangladesh, Ethiopia, India and Nigeria (BGD, ETH, IND, NGA) have no rows for 2000–2010. They are not in the balanced set.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows → 416 after removing duplicates → 416 after the year-window filter → 415 after dropping the missing cell → 345 after keeping only balanced countries (`n_analysed`). The balanced set has 15 countries (`n_countries_balanced`, `balanced_countries`: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA). 5 countries are incomplete (`n_countries_incomplete`).\n\n## Method\nFor each year I took the simple mean over the 15 balanced countries (`out/analysis.py`; `out/balanced_means_by_year.csv` is a supplementary output). The change is the 2022 mean minus the 2000 mean. The baseline is the mean over all distinct countries with a recorded 2022 value (20 countries, `n_countries_2022_available`), with TUR counted once. The method uses no randomness (`random_seed` = null).\n\n## Results\n- Balanced mean in 2000: **78.060 years** (`mean_2000_balanced` = 78.060267).\n- Balanced mean in 2022: **81.553 years** (`mean_2022_balanced` = 81.553067).\n- Change from 2000 to 2022: **+3.493 years** (`change_2000_2022_balanced` = 3.4928).\n- Baseline, mean over all 20 countries with a 2022 value: 78.692 years (`mean_2022_all_available`). This is lower than the balanced 2022 mean because four of the five excluded countries (BGD, ETH, IND, NGA) have lower life expectancy. The baseline is not a measure of change over time.\n- Feasibility: `feasibility` = \"complete\".\n\n## Uncertainty\nThese are descriptive means over a fixed set of countries; they are not estimates from a sample. I therefore report no sampling standard errors or confidence intervals. The remaining uncertainty is measurement and revision error in the WDI figures, which I cannot quantify from this file. I checked the figures independently with a pivot/dropna recomputation, which matched exactly, and a re-run of the script gave byte-identical output (`out/state/checks.md`).\n\n## Limitations and open issues\n- The balanced set contains only high- and upper-middle-income countries (all OECD members). Switzerland is excluded because of a single missing year (2007), and four lower-income countries are excluded because they have no data before 2011. The result therefore does not represent all 20 countries.\n- The countries are equally weighted, not weighted by population.\n- I resolved the TUR duplicates only because both copies agree exactly. The script stops if a duplicate pair ever has conflicting values.\n- Year-on-year jumps of more than 1.5 years occur in 2020–2022 (e.g. ESP, USA, IND, TUR, BGD). I kept them unchanged, since the task allows no external lookup. They are consistent with COVID-era mortality shocks and recovery.\n# Data provenance — F1-conf-02\n\n`life_expectancy_panel.csv` — observed data (not simulated): **Life expectancy at birth, total (years)**, indicator `SP.DYN.LE00.IN`, World Development Indicators (World Bank, source id 2, release last updated 2026-10-08), for 20 countries (Australia, Austria, Bangladesh, Belgium, Canada, Ethiopia, France, Germany, India, Italy, Japan, Korea Rep., Netherlands, Nigeria, Spain, Sweden, Switzerland, Türkiye, United Kingdom, United States), years 2000–2022.\n\n- Source URL (JSON API): `https://api.worldbank.org/v2/country/CAN;USA;GBR;FRA;DEU;ITA;ESP;JPN;KOR;AUS;SWE;NLD;CHE;BEL;AUT;TUR;IND;NGA;ETH;BGD/indicator/SP.DYN.LE00.IN?format=json&per_page=2000&date=2000:2022`\n- Retrieved: 2026-10-30T03:25Z. SHA-256 of the raw API response: `ead88ee94d5b6cc6f8a055c9f2ec79f232c37faf8baca0ea122dbfc0550ff27e`\n- License: World Bank open data, Creative Commons Attribution 4.0 (CC BY-4.0).\n- Columns: `country_name` (string), `iso3` (ISO-3166 alpha-3 code as returned by the API), `year` (integer), `life_expectancy` (years, 3 decimals). Values were rounded to 3 decimals; nothing else was changed in the values.\n- The panel was assembled by the task author from two extractions of the API response (one earlier, narrower extraction and one later, wider one) and may contain assembly errors. An empty `life_expectancy` cell means no value was recorded for that country-year in the file.\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"8813e08d-83d3-4a4a-bf09-ec5dcbbccbcf","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030","hash":"8eac2eff671c4a01cc8a1bacc39c7d04348ab5f8b8972a39bc0ac2f9eaa06d44","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01CZqaLG3LbNrjrn6hULxtVM","name":"Bash","input":{"command":"cat out/analysis.py; cat out/exclusions.csv; cat out/report.md; cat data/README.md; head -3 data/life_expectancy_panel.csv","description":"Read code, ledger, 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b['2022'].mean(), w['2022'].mean(), w['2022'].notna().sum())\nE","description":"Rerun in scratch and independently check data"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5000,"cache_read_input_tokens":5980,"output_tokens":696,"output_tokens_details":{"thinking_tokens":69},"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":5000},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXozE3UKtqR91BZqmhyc","requestRef":"52480d6c-70c1-4d9b-9921-abe70cea069a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e353be45-e337-49d6-8e72-08de953e1c01","timestamp":"2026-10-30T04:43:58.713Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030","version":"2.1.314","gitBranch":"HEAD"}25{"type":"last-prompt","lastPrompt":"Review","leafUuid":"e353be45-e337-49d6-8e72-08de953e1c01","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030"}26{"type":"atis-latch","atis":"","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030"}27{"parentUuid":"e353be45-e337-49d6-8e72-08de953e1c01","isSidechain":false,"promptId":"04da1ee1-3959-497a-a2b1-483edc2ed797","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Jd3JrfMLA3vT2ct6FaESFe","type":"tool_result","content":"same results.json\nsame analytic.csv\nsame exclusions.csv\n(439, 4)\niso3\n'TUR' 46\n'AUS' 23\n'AUT' 23\n'GBR' 23\n'CHE' 23\n'SWE' 23\n'ESP' 23\n'NLD' 23\n'KOR' 23\n'JPN' 23\n'ITA' 23\n'DEU' 23\n'FRA' 23\n'CAN' 23\n'BEL' 23\n'USA' 23\n'IND' 12\n'NGA' 12\n'ETH' 12\n'BGD' 12\nyear\n2000 17\n2001 17\n2002 17\n2003 17\n2004 17\n2005 17\n2006 17\n2007 17\n2008 17\n2009 17\n2010 17\n2011 21\n2012 21\n2013 21\n2014 21\n2015 21\n2016 21\n2017 21\n2018 21\n2019 21\n2020 21\n2021 21\n2022 21\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 \ncount 438.000000\nmean 78.466521\nstd 6.073816\nmin 51.490000\n25% 77.738000\n50% 80.517500\n75% 81.803500\nmax 84.560000\nName: life_expectancy, dtype: float64\niso3\nAUS [Australia]\nAUT [Austria]\nBEL [Belgium]\nBGD [Bangladesh]\nCAN [Canada]\nCHE [Switzerland]\nDEU [Germany]\nESP [Spain]\nETH [Ethiopia]\nFRA [France]\nGBR [United Kingdom]\nIND [India]\nITA [Italy]\nJPN [Japan]\nKOR [Korea, Rep.]\nNGA [Nigeria]\nNLD [Netherlands]\nSWE [Sweden]\nTUR [Turkey, Turkiye]\nUSA [United States]\nName: country_name, dtype: object\n1\niso3\nAUS 23\nAUT 23\nBEL 23\nBGD 12\nCAN 23\nCHE 22\nDEU 23\nESP 23\nETH 12\nFRA 23\nGBR 23\nIND 12\nITA 23\nJPN 23\nKOR 23\nNGA 12\nNLD 23\nSWE 23\nTUR 23\nUSA 23\ndtype: int64\n15 78.06026666666666 81.55306666666665 78.69199999999998 20\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0145","is_error":false}]},"uuid":"ee546751-4dbe-4fdc-a7d2-604e3a858bbe","timestamp":"2026-10-30T04:43:59.325Z","toolUseResult":{"stdout":"same results.json\nsame analytic.csv\nsame exclusions.csv\n(439, 4)\niso3\n'TUR' 46\n'AUS' 23\n'AUT' 23\n'GBR' 23\n'CHE' 23\n'SWE' 23\n'ESP' 23\n'NLD' 23\n'KOR' 23\n'JPN' 23\n'ITA' 23\n'DEU' 23\n'FRA' 23\n'CAN' 23\n'BEL' 23\n'USA' 23\n'IND' 12\n'NGA' 12\n'ETH' 12\n'BGD' 12\nyear\n2000 17\n2001 17\n2002 17\n2003 17\n2004 17\n2005 17\n2006 17\n2007 17\n2008 17\n2009 17\n2010 17\n2011 21\n2012 21\n2013 21\n2014 21\n2015 21\n2016 21\n2017 21\n2018 21\n2019 21\n2020 21\n2021 21\n2022 21\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 \ncount 438.000000\nmean 78.466521\nstd 6.073816\nmin 51.490000\n25% 77.738000\n50% 80.517500\n75% 81.803500\nmax 84.560000\nName: life_expectancy, dtype: float64\niso3\nAUS [Australia]\nAUT [Austria]\nBEL [Belgium]\nBGD [Bangladesh]\nCAN [Canada]\nCHE [Switzerland]\nDEU [Germany]\nESP [Spain]\nETH [Ethiopia]\nFRA [France]\nGBR [United Kingdom]\nIND [India]\nITA [Italy]\nJPN [Japan]\nKOR [Korea, Rep.]\nNGA [Nigeria]\nNLD [Netherlands]\nSWE [Sweden]\nTUR [Turkey, Turkiye]\nUSA [United States]\nName: country_name, dtype: object\n1\niso3\nAUS 23\nAUT 23\nBEL 23\nBGD 12\nCAN 23\nCHE 22\nDEU 23\nESP 23\nETH 12\nFRA 23\nGBR 23\nIND 12\nITA 23\nJPN 23\nKOR 23\nNGA 12\nNLD 23\nSWE 23\nTUR 23\nUSA 23\ndtype: int64\n15 78.06026666666666 81.55306666666665 78.69199999999998 20","stderr":"\nShell cwd was reset to 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b['2022'].mean(), w['2022'].mean(), w['2022'].notna().sum())\nE","description":"Rerun in scratch and independently check 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But one of the five excluded countries, CHE, has high life expectancy and pushes the baseline up. The report states this correctly ('four of the five excluded countries'); the claim text overgeneralises.\",\n \"suggested_fix\": \"Reword C3 to say that the four excluded lower-income countries (BGD, ETH, IND, NGA) pull the baseline down, while CHE, also excluded, partly offsets this.\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"analysis.py (absent-years data_issues)\",\n \"issue\": \"Absent years are reported as the range min(absent)-max(absent), which would mislabel gaps that are not contiguous. In this file each gap is contiguous (2000-2010), so the output is correct.\",\n \"suggested_fix\": \"List the absent years explicitly, or group them into contiguous runs.\"\n }\n ],\n \"summary\": \"I re-ran analysis.py in a scratch copy and it reproduced results.json, analytic.csv and exclusions.csv byte for byte. A separate pivot/dropna recomputation from the raw CSV gave the same answer: 20 countries, 15 balanced, 345 country-years, means 78.060267 (2000) and 81.553067 (2022), change 3.4928, and an all-available 2022 baseline of 78.692 over 20 countries with TUR counted once. The code finds and documents every assembly problem in the file. TUR appears twice for all 23 years under two names, with identical values; the copies are collapsed by iso3. CHE has an empty 2007 cell. BGD, ETH, IND and NGA have no rows for 2000-2010. Nothing is imputed and only the data file is used. The exclusion ledger chains correctly from 439 rows to 345. All required keys are present. The uncertainty and limitations sections are honest: there is no sampling inference, the countries are unweighted, and the balanced set leaves out lower-income countries. There are only minor wording and robustness points.\"\n}\nE\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok","description":"Check TUR names in analytic and write review"}}]}}34{"type":"api-request","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030","id":"81d3a85d-99e2-4a44-87ce-5e1651e4e615","timestamp":"2026-10-30T04:44:13.332Z","version":"2.1.314","querySource":"sdk","shapeHash":"782bc36e4b61d4dd39e15fbdc180c2db849843603b97554fd1cd1044b0c5ed05","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":"30b9d237-de22-4c3e-ab7c-485a38e01a00","keep":8,"tail":["db835608a958097b1bacb81259be7ba0c9434d4c9aa2d7fe574837a9907d8cca","30a82051a9483103e3a5b9ea466c94fe47f1a5ff0f5d9145ea3957089fdd6507"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"741dec5a8b1e7f909d04c1d9c031ea08b2b5c1d7fb4bb0489f4684e1e668002a"}35{"parentUuid":"c452091a-c2ad-448b-a801-11916b8933d8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp1mEyPcxVf9giqbPy5","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1308,"cache_read_input_tokens":12531,"output_tokens":733,"output_tokens_details":{"thinking_tokens":33},"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":1308},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6064,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp1kybkUyph4bUnPvr2","requestRef":"81d3a85d-99e2-4a44-87ce-5e1651e4e615","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b2bea90b-ce88-4a9a-b889-2a2205c193fa","timestamp":"2026-10-30T04:44:21.847Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"b2bea90b-ce88-4a9a-b889-2a2205c193fa","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp1mEyPcxVf9giqbPy5","type":"message","role":"assistant","content":[{"type":"text","text":"I recommend **accept**, with two minor issues. The review is saved to `out/review/review.json`.\n\nI re-ran `out/analysis.py` in a scratch copy, and it reproduced `results.json`, `analytic.csv` and `exclusions.csv` byte for byte. I also recomputed everything directly from the raw CSV and got the same numbers:\n- **Countries:** 20 in total, 15 with a value in every year (balanced), 5 incomplete.\n- **Rows analysed:** 345 country-years.\n- **Balanced means:** 78.060267 years in 2000 and 81.553067 in 2022, a change of +3.4928 years.\n- **2022 baseline:** 78.692 years over all 20 countries with a 2022 value, with Türkiye (TUR) counted once.\n\nThe code finds and documents every data problem in the file:\n- **TUR duplicated:** every year appears twice, under \"Turkey\" and \"Turkiye\", with identical values. The copies are merged by country code.\n- **Switzerland (CHE):** the 2007 value is empty.\n- **Bangladesh, Ethiopia, India, Nigeria (BGD/ETH/IND/NGA):** no rows at all for 2000–2010.\n\nThe code reads only the data file and does not fill in any missing values. The exclusion ledger adds up correctly at every step from 439 rows to 345. All required keys are present. The uncertainty and limitations sections are honest: the means are descriptive with no sampling error, countries are not weighted by population, and the balanced set leaves out the lower-income countries.\n\nThe two minor issues:\n1. **Claim C3 overgeneralises.** It says the baseline is lower because low-life-expectancy countries are left out of the balanced set. But Switzerland, also left out, has high life expectancy and pulls the baseline up. The report itself words this correctly (\"four of the five excluded countries\").\n2. **Gap labels could be wrong on other data.** The script describes missing years as a single first-to-last range, which would mislabel gaps with breaks in them. Every gap in this file is one continuous stretch, so the output here is correct."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1308,"cache_read_input_tokens":12531,"output_tokens":733,"output_tokens_details":{"thinking_tokens":33},"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":1308},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp1kybkUyph4bUnPvr2","requestRef":"81d3a85d-99e2-4a44-87ce-5e1651e4e615","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6a8e2d5e-d8b9-4f36-bd39-00be94bc8f78","timestamp":"2026-10-30T04:44:21.849Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030","version":"2.1.314","gitBranch":"HEAD"}37{"type":"last-prompt","lastPrompt":"Review","leafUuid":"6a8e2d5e-d8b9-4f36-bd39-00be94bc8f78","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030"}38{"type":"cost-state","sessionId":"681772b6-0728-4c87-98ac-e3d288c2d030","totalCostUSD":0.127806,"totalAPIDuration":32532,"totalAPIDurationWithoutRetries":32528,"totalToolDuration":834,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":33534,"startTime":1793335428323,"modelUsage":{"claude-opus-5-5":{"inputTokens":12,"outputTokens":2972,"thinkingTokens":466,"cacheReadInputTokens":33215,"cacheCreationInputTokens":12335,"cacheCreationEphemeral5mInputTokens":12335,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.127806}},"hasUnknownModelCost":false}39