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N11: annotation pilot sampling frames (seed 20261105), weights, design and protocol; no labels produced

Items: FIRC0001-ECON-04, EDIT-01 (c), ADVE-01 (i) (frames only; metrics = author task AD-2).
192 productive strata (item group x run x truncation x confidence x category), n = 1,677 with floor 10 (nested core subsample 510) + 385 zero-mention sections
with inclusion probability ~ gate x log length; dev/holdout 1,058 / 1,004; CI half-width +-4.4 pp (precision), +-2.4 pp (false-negative rate at 5%).
Full-text frames and the sealed model output stay on M3U96b (~/firc0001_review/frames/); worktree CSVs truncate text to 2,000 chars. docs/annotation_protocol.md (new).

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 5, 2026) parent b7e79df Browse files

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added code/audit/a11_annotation_frame.py +148 −0
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1+"""N11 - sampling frames and design for the human annotation pilot (cluster A, ECON-04). No labels produced.
2+
3+Strata: item group x run x truncation bin (N5 shown fraction) x productive x confidence bin x category group;
4+productive sections n = 385 allocated proportionally with a floor of 10 and 2x oversampling of the 0.45-0.7 bin;
5+zero-mention sections drawn with inclusion probabilities proportional to (gate pass, log length) with
6+inverse-probability weights; seeded (20261105); development / holdout split. Frames with full text and
7+_focus(text) stay on the node (~/firc0001_review/frames/); the worktree copy truncates text to 2,000 chars and
8+carries no model output (the model's mentions are sealed in a separate file).
9+"""
10+from __future__ import annotations
11+import json, sys, math
12+from pathlib import Path
13+import numpy as np
14+sys.path.insert(0, str(Path(__file__).resolve().parent))
15+from common import *
16+from spid.extract.llm import _focus
17+
18+N_PROD = 385; N_ZERO = 385; FLOOR = 10
19+rng = np.random.default_rng(SEED)
20+r = Run("annotation_frame")
21+FR = WORK / "frames"; FR.mkdir(exist_ok=True)
22+con = connect(SPID_DB)
23+tmin = con.execute("select min(extracted_at) from extracted_sections").fetchone()[0]
24+feat = RESULTS / "focus" / "section_features.parquet"
25+con.execute(f"CREATE TEMP TABLE ft AS SELECT section_id, nchar, gate_py, shown_chars*1.0/nchar shown_frac FROM read_parquet('{feat}')")
26+con.execute(f"""CREATE TEMP TABLE S AS
27+SELECT e.section_id, e.accession_number, e.n_mentions, CASE WHEN e.extracted_at = TIMESTAMP '{tmin}' THEN 'june' ELSE 'october' END run,
28+ f.form_type, f.cik, f.ticker, f.company_name, f.filing_date, m.section_name item, m.word_count, ft.nchar, ft.gate_py gate, ft.shown_frac,
29+ CASE WHEN f.form_type='10-K' AND m.section_name='md_and_a' THEN '10-K md_and_a' WHEN f.form_type='10-K' THEN '10-K other'
30+ WHEN f.form_type='10-Q' AND m.section_name='md_and_a' THEN '10-Q md_and_a' WHEN f.form_type='10-Q' THEN '10-Q other' ELSE '8-K' END item_group,
31+ CASE WHEN ft.shown_frac >= 0.9 THEN 'shown>=0.9' WHEN ft.shown_frac >= 0.25 THEN 'shown0.25-0.9' ELSE 'shown<0.25' END trunc_bin,
32+ pm.max_conf, CASE WHEN pm.max_conf IS NULL THEN 'none' WHEN pm.max_conf <= 0.7 THEN 'conf0.45-0.7' ELSE 'conf>0.7' END conf_bin,
33+ CASE WHEN pm.types IS NULL THEN 'none' WHEN list_contains(pm.types, 'ma_integration') THEN 'ma_integration'
34+ WHEN len(list_intersect(pm.types, ['capex_program','plant_construction','manufacturing_expansion','infrastructure','data_center'])) > 0 THEN 'capex-type'
35+ WHEN len(list_intersect(pm.types, ['technology_deployment','ai_initiative','digital_transformation','erp_implementation','cloud_migration','automation'])) > 0 THEN 'technology-type'
36+ ELSE 'other' END cat_group
37+FROM extracted_sections e JOIN src_filings f ON f.accession_number = e.accession_number
38+JOIN (SELECT section_id, section_name, word_count FROM src_sections UNION ALL SELECT section_id, section_name, word_count FROM spid_sections) m USING (section_id)
39+LEFT JOIN ft USING (section_id)
40+LEFT JOIN (SELECT section_id, max(confidence) max_conf, list_distinct(list(project_type)) types FROM project_mentions GROUP BY 1) pm USING (section_id)""")
41+rules = RESULTS / "rules" / "rules_mentions.parquet"
42+if rules.exists():
43+ con.execute(f"CREATE TEMP TABLE rl AS SELECT section_id, rules_n FROM read_parquet('{rules}')")
44+ con.execute("ALTER TABLE S ADD COLUMN rules_n INTEGER"); con.execute("UPDATE S SET rules_n = rl.rules_n FROM rl WHERE S.section_id = rl.section_id")
45+else:
46+ con.execute("ALTER TABLE S ADD COLUMN rules_n INTEGER")
47+# ---- productive strata and allocation ---------------------------------------------------------------------------
48+strata = con.execute("""SELECT item_group, run, trunc_bin, conf_bin, cat_group, count(*) N FROM S WHERE n_mentions > 0 GROUP BY 1,2,3,4,5 ORDER BY 1,2,3,4,5""").fetchall()
49+Ntot = sum(s[5] for s in strata)
50+alloc = []
51+for ig, rn, tb, cb, cg, N in strata:
52+ share = N / Ntot * (2.0 if cb == "conf0.45-0.7" else 1.0)
53+ n = max(FLOOR, round(N_PROD * share)); n = min(n, N)
54+ alloc.append((ig, rn, tb, cb, cg, N, n, N / n))
55+n_prod_actual = sum(a[6] for a in alloc)
56+r.write_csv("design_productive_strata.csv", ["item_group", "run", "trunc_bin", "conf_bin", "cat_group", "N_population", "n_sampled", "weight"], alloc)
57+# ---- draw productive --------------------------------------------------------------------------------------------
58+rows = []
59+for ig, rn, tb, cb, cg, N, n, w in alloc:
60+ ids = con.execute("SELECT section_id FROM S WHERE n_mentions > 0 AND item_group=? AND run=? AND trunc_bin=? AND conf_bin=? AND cat_group=? ORDER BY section_id", [ig, rn, tb, cb, cg]).fetchall()
61+ pick = rng.choice(len(ids), size=n, replace=False)
62+ for i in pick:
63+ rows.append((ids[i][0], "productive", f"{ig}|{rn}|{tb}|{cb}|{cg}", w))
64+# ---- draw zero-mention with inclusion probabilities ∝ (gate, log length) -------------------------------------------
65+zero = con.execute("SELECT section_id, gate, nchar, item_group, run, trunc_bin FROM S WHERE n_mentions = 0 ORDER BY section_id").fetchall()
66+score = np.array([(2.0 if g else 1.0) * (1.0 + math.log10(max(30, nc))) for _, g, nc, *_ in zero])
67+p = score / score.sum()
68+pick = rng.choice(len(zero), size=N_ZERO, replace=False, p=p)
69+pi = N_ZERO * p # first-order inclusion probability (approximation, sampling fraction 4e-4)
70+for i in pick:
71+ sid, g, nc, ig, rn, tb = zero[i]
72+ rows.append((sid, "zero", f"{ig}|{rn}|{tb}|gate={'pass' if g else 'reject'}", 1.0 / pi[i]))
73+con.execute("CREATE TEMP TABLE pick AS SELECT * FROM (VALUES " + ",".join(["(?,?,?,?)"] * len(rows)) + ") t(section_id, frame, stratum, weight)", [x for row in rows for x in row])
74+# dev / holdout split, stratified by frame x stratum, seeded
75+sel = con.execute("SELECT p.section_id, p.frame, p.stratum, p.weight, s.accession_number, s.form_type, s.item, s.cik, s.ticker, s.company_name, s.filing_date, s.run, s.word_count, s.nchar, s.gate, s.shown_frac, s.n_mentions, s.max_conf, s.cat_group, s.rules_n FROM pick p JOIN S s USING (section_id) ORDER BY p.frame, p.stratum, p.section_id").fetchall()
76+split = {}
77+by = {}
78+for x in sel: by.setdefault((x[1], x[2]), []).append(x[0])
79+for k, ids in by.items():
80+ perm = rng.permutation(len(ids))
81+ for j, i in enumerate(perm):
82+ split[ids[i]] = "dev" if j % 2 == 0 else "holdout"
83+cols = ["section_id", "frame", "stratum", "weight", "accession_number", "form_type", "item", "cik", "ticker", "company_name", "filing_date", "run", "word_count", "nchar", "gate_pass", "shown_frac", "n_mentions_model", "max_conf_model", "cat_group_model", "rules_n"]
84+# nested core subsample (first annotation pass): ~385 productive sections, proportional to the stratum population, min 1, seeded
85+core = set()
86+prod_by = {}
87+for x in sel:
88+ if x[1] == "productive": prod_by.setdefault(x[2], []).append(x[0])
89+for st, ids in prod_by.items():
90+ N_h = next(a[5] for a in alloc if f"{a[0]}|{a[1]}|{a[2]}|{a[3]}|{a[4]}" == st)
91+ k = max(1, min(len(ids), round(385 * N_h / Ntot)))
92+ for i in rng.choice(len(ids), size=k, replace=False): core.add(ids[i])
93+for x in sel:
94+ if x[1] == "zero": core.add(x[0])
95+r.write_csv("weights.csv", ["section_id", "frame", "stratum", "weight", "split", "core"], [(x[0], x[1], x[2], round(x[3], 4), split[x[0]], x[0] in core) for x in sel])
96+# worktree copies: no model output, text truncated to 2,000 chars
97+con.execute("CREATE TEMP TABLE txt AS SELECT t.section_id, t.text FROM (SELECT section_id, text FROM src_sections UNION ALL SELECT section_id, text FROM spid_sections) t JOIN pick USING (section_id)")
98+texts = dict(con.execute("SELECT section_id, text FROM txt").fetchall())
99+pub_cols = ["section_id", "split", "core", "frame", "stratum", "weight", "accession_number", "form_type", "item", "cik", "ticker", "company_name", "filing_date", "run", "word_count", "nchar", "gate_pass", "shown_frac", "text_2000"]
100+pub = [(x[0], split[x[0]], x[0] in core, x[1], x[2], round(x[3], 4), x[4], x[5], x[6], x[7], x[8], x[9], str(x[10]), x[11], x[12], x[13], x[14], round(x[15], 4) if x[15] is not None else None, (texts.get(x[0]) or "")[:2000]) for x in sel]
101+r.write_csv("frame_dev.csv", pub_cols, [p_ for p_ in pub if p_[1] == "dev"])
102+r.write_csv("frame_holdout.csv", pub_cols, [p_ for p_ in pub if p_[1] == "holdout"])
103+# node-only full frames + sealed model output
104+men = {}
105+for row in con.execute("SELECT section_id, mention_id, project_name, project_type, description, status, amount_usd, locations, partners, years, confidence FROM project_mentions WHERE section_id IN (SELECT section_id FROM pick)").fetchall():
106+ men.setdefault(row[0], []).append(dict(zip(["mention_id", "project_name", "project_type", "description", "status", "amount_usd", "locations", "partners", "years", "confidence"], row[1:])))
107+for name in ("dev", "holdout"):
108+ with open(FR / f"frame_{name}.jsonl", "w") as fh:
109+ for x in sel:
110+ if split[x[0]] != name: continue
111+ t = texts.get(x[0]) or ""
112+ fh.write(json.dumps({"section_id": x[0], "frame": x[1], "stratum": x[2], "weight": x[3], "accession_number": x[4], "form_type": x[5], "item": x[6], "cik": x[7], "ticker": x[8],
113+ "company_name": x[9], "filing_date": str(x[10]), "run": x[11], "word_count": x[12], "nchar": x[13], "gate_pass": x[14], "shown_frac": x[15],
114+ "text": t, "focus_text_seen_by_model": ("SECTION: " + str(x[6]) + "\n\n" + _focus(t))[:14000]}, default=str) + "\n")
115+with open(FR / "model_output_SEALED.jsonl", "w") as fh:
116+ for x in sel:
117+ fh.write(json.dumps({"section_id": x[0], "split": split[x[0]], "n_mentions": x[16], "mentions": men.get(x[0], [])}, default=str) + "\n")
118+import shutil
119+shutil.copy(r.out / "weights.csv", FR / "weights.csv")
120+# ---- design numbers: CI widths and MDE ---------------------------------------------------------------------------------
121+w_prod = np.array([x[3] for x in sel if x[1] == "productive"]); w_zero = np.array([x[3] for x in sel if x[1] == "zero"])
122+deff = lambda w: float(1 + (w.std() / w.mean()) ** 2)
123+D = {"seed": SEED, "n_productive": int(len(w_prod)), "n_zero": int(len(w_zero)), "n_strata_productive": len(alloc), "floor": FLOOR, "oversample_conf_0.45_0.7": 2.0,
124+ "population_productive": Ntot, "population_zero": len(zero), "deff_productive": deff(w_prod), "deff_zero": deff(w_zero),
125+ "half_width_95_precision_p0.5": round(1.96 * math.sqrt(0.25 / len(w_prod) * deff(w_prod)), 4),
126+ "half_width_95_precision_p0.8": round(1.96 * math.sqrt(0.16 / len(w_prod) * deff(w_prod)), 4),
127+ "half_width_95_zero_fn_rate": {str(p0): round(1.96 * math.sqrt(p0 * (1 - p0) / len(w_zero) * deff(w_zero)), 4) for p0 in (0.02, 0.05, 0.10)},
128+ "mde_gate_recall_note": "false-negative rate among zero-mention sections estimated with inverse-probability weights; MDE (80% power, two-sided 5%) for a difference between gate-pass and gate-reject strata of size n1, n2: 2.8*sqrt(p(1-p)*(1/n1+1/n2)*deff)",
129+ "zero_frame_gate_pass": int(sum(1 for x in sel if x[1] == "zero" and x[14])), "zero_frame_gate_reject": int(sum(1 for x in sel if x[1] == "zero" and not x[14])),
130+ "split": {"dev": sum(1 for v in split.values() if v == "dev"), "holdout": sum(1 for v in split.values() if v == "holdout")},
131+ "core_subsample": {"productive": sum(1 for x in sel if x[1] == "productive" and x[0] in core), "zero": sum(1 for x in sel if x[1] == "zero" and x[0] in core),
132+ "note": "nested first-pass subsample drawn proportionally to the stratum population within the frame (weights = N_h / n_h,core); the full frame keeps the floor-of-10 design"},
133+ "rules_column_available": rules.exists(),
134+ "files_on_node": [str(FR / "frame_dev.jsonl"), str(FR / "frame_holdout.jsonl"), str(FR / "model_output_SEALED.jsonl"), str(FR / "weights.csv")]}
135+p0 = 0.05
136+for n1, n2 in ((D["zero_frame_gate_pass"], D["zero_frame_gate_reject"]),):
137+ D["mde_gate_recall_diff_at_p0.05"] = round(2.8 * math.sqrt(p0 * (1 - p0) * (1 / max(1, n1) + 1 / max(1, n2)) * deff(w_zero)), 4)
138+r.write_json("design.json", D)
139+r.dump("frame_composition.csv", con, "SELECT frame, stratum, count(*) n, round(sum(weight)) weighted_n FROM pick GROUP BY 1,2 ORDER BY 1,2")
140+with open(FR / "README.md", "w") as fh:
141+ fh.write(f"""# Annotation pilot frames (FIRC-0001, N11) — seed {SEED}, drawn {dt.date.today()}
142+
143+frame_dev.jsonl / frame_holdout.jsonl: one JSON object per section with the full section text and `focus_text_seen_by_model`
144+(exactly what the extractor sent, llm.py:125-126). Annotators label the TEXT; they never see model_output_SEALED.jsonl
145+until the labels are frozen. weights.csv carries the inverse-probability weight and the split. design.json documents n,
146+strata, weights, CI half-widths and the MDE. Protocol: docs/annotation_protocol.md in the paper repository.
147+""")
148+r.finish()
added docs/annotation_protocol.md +32 −0
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1+# Annotation protocol for the SPID human-validation pilot (Stage D, package N11)
2+
3+*Status: protocol and sampling frames delivered (2026-11-05); no label has been produced. The metrics of this protocol are the author task AD-2 (`improvements/FIRC-0001/authorization_requests.md`). Table 10 gate (d) therefore reads "not met — protocol revised, frame drawn, annotation pending".*
4+
5+## 1. Objects and population
6+
7+- **Unit:** one ledgered section (`extracted_sections`, 1,123,098 rows), i.e. exactly the text block the extractor received a verdict for. Two populations: **productive** sections (`n_mentions > 0`, N = 168,230) and **zero-mention** sections (N = 954,868; 419,361 of them never reached the model because `_PROJECT_GATE` rejected them).
8+- **What the annotator sees:** the full section text and, separately, `focus_text_seen_by_model` — the string actually sent (`"SECTION: <item>\n\n" + _focus(text)`, cut at 14,000 characters; `llm.py:125-126`). Both are in `~/firc0001_review/frames/frame_{dev,holdout}.jsonl` (M3U96b). The worktree copies (`results/annotation_frame/frame_{dev,holdout}.csv`) truncate the text to 2,000 characters and are for inspection only.
9+- **What the annotator must not see until labels are frozen:** the model's mentions (`frames/model_output_SEALED.jsonl`).
10+
11+## 2. Sampling design (seed 20261105; `code/audit/a11_annotation_frame.py`)
12+
13+- **Productive frame.** Strata = item group (10-K MD&A / other 10-K / 10-Q MD&A / other 10-Q / 8-K) × run (June backfilled / October) × truncation bin from N5 (shown fraction ≥ 0.9 / 0.25–0.9 / < 0.25) × section confidence bin (max mention confidence ≤ 0.7 / > 0.7) × category group (ma_integration / capex-type / technology-type / other). Allocation: proportional to the stratum population with a **floor of 10** and a **2× oversampling of the ≤ 0.7 bin**; strata smaller than the floor are taken whole. Result: 192 non-empty strata, **n = 1,677** (the floor dominates); weights w_h = N_h / n_h (`design_productive_strata.csv`, `weights.csv`). A nested **core subsample of ≈ 385 sections** (column `core`, proportional within the drawn strata, min 1 per stratum) is the recommended first pass; its weights are N_h / n_h,core.
14+- **Zero-mention frame.** n = 385 drawn without replacement with inclusion probability ∝ (2 if gate passed else 1) × (1 + log10 characters), so that long gate-passing sections (where a missed project is most likely) are over-represented; 285 gate-pass / 100 gate-reject in the draw; inverse-probability weights 1/π_i stored.
15+- **Split.** Every stratum is split at random into *development* (guide refinement, disagreement resolution) and *holdout* (reported metrics): 1,058 / 1,004 sections.
16+- **Precision of the design** (`design.json`): design effect 3.4 (productive) / 1.2 (zero); 95 % half-width ± 4.4 pp for a section-level precision near 0.5 (± 3.5 pp near 0.8) on the full productive frame; ± 2.4 pp for a false-negative rate near 5 % on the zero frame; MDE (80 % power, two-sided 5 %) of a gate-pass vs gate-reject difference in the false-negative rate ≈ 7.8 pp at p = 0.05.
17+
18+## 3. Labels
19+
20+Per section (both frames): **S1** does the text describe at least one concrete initiative under the construct of §5.1 (yes / no / unsure); **S2** list of initiatives found (free text, ≤ 10). Per model mention (productive frame, after unsealing): **M1** the mention corresponds to an initiative in the text (correct / partly / wrong / construct violation — e.g. share repurchase, financing instrument, dividend); **M2** `project_type` correct under the 21-category taxonomy (Appendix A); **M3** each structured field (years, locations, partners, amount) is literally supported by the text (the mechanical rates of N2 are the comparison point); **M4** status correct. Per project (linkage audit, optional): **L1** two mentions resolved to the same `project_id` describe the same initiative (same / different / unsure).
21+
22+## 4. Procedure
23+
24+1. Two annotators, trained on 40 development sections, label independently; the guide is revised once after the development set and frozen before the holdout.
25+2. Blinding: S1–S2 are labelled before `model_output_SEALED.jsonl` is opened; M1–M4 afterwards. The order of sections is shuffled; the stratum, run and confidence are hidden.
26+3. Agreement: Krippendorff's α (nominal) for S1, M1, M2; prevalence-adjusted bias-adjusted κ (PABAK) reported next to Cohen's κ because S1 is rare in the zero frame. Disagreements adjudicated by a third reader; adjudicated labels are the reference.
27+4. Estimators (holdout only, inverse-probability weighted): section-level precision P(S1 = yes | productive), mention-level precision P(M1 = correct), category accuracy P(M2 | M1 = correct), field-support rates (compare with N2), false-negative rate P(S1 = yes | zero) overall and by gate status (gate recall bound), construct-violation share by item (compare with N6). 95 % intervals by stratified bootstrap of sections (1,000 replicates).
28+5. Reporting: Table 10 gate (d) becomes "met / not met" on the holdout numbers; §8.2 prints n per stratum, α / PABAK, and every estimate with its interval; the development-set numbers are reported separately and never pooled.
29+
30+## 5. Files
31+
32+`results/annotation_frame/{design.json, design_productive_strata.csv, frame_composition.csv, weights.csv, frame_dev.csv, frame_holdout.csv}` (worktree); `~/firc0001_review/frames/{frame_dev.jsonl, frame_holdout.jsonl, model_output_SEALED.jsonl, weights.csv, README.md}` (M3U96b, full text). The X1 stability run (AR-LLM-1), if authorised, should reuse the same 500-section stratified subset of this frame so that determinism and human agreement are measured on identical sections.
added results/annotation_frame/design.json +39 −0
@@ -0,0 +1,39 @@
1+{
2+ "seed": 20261105,
3+ "n_productive": 1677,
4+ "n_zero": 385,
5+ "n_strata_productive": 192,
6+ "floor": 10,
7+ "oversample_conf_0.45_0.7": 2.0,
8+ "population_productive": 168230,
9+ "population_zero": 954868,
10+ "deff_productive": 3.4131363613604115,
11+ "deff_zero": 1.2130239948560366,
12+ "half_width_95_precision_p0.5": 0.0442,
13+ "half_width_95_precision_p0.8": 0.0354,
14+ "half_width_95_zero_fn_rate": {
15+ "0.02": 0.0154,
16+ "0.05": 0.024,
17+ "0.1": 0.033
18+ },
19+ "mde_gate_recall_note": "false-negative rate among zero-mention sections estimated with inverse-probability weights; MDE (80% power, two-sided 5%) for a difference between gate-pass and gate-reject strata of size n1, n2: 2.8*sqrt(p(1-p)*(1/n1+1/n2)*deff)",
20+ "zero_frame_gate_pass": 285,
21+ "zero_frame_gate_reject": 100,
22+ "split": {
23+ "dev": 1058,
24+ "holdout": 1004
25+ },
26+ "core_subsample": {
27+ "productive": 510,
28+ "zero": 385,
29+ "note": "nested first-pass subsample drawn proportionally to the stratum population within the frame (weights = N_h / n_h,core); the full frame keeps the floor-of-10 design"
30+ },
31+ "rules_column_available": true,
32+ "files_on_node": [
33+ "/Users/simon-pierreboucher/firc0001_review/frames/frame_dev.jsonl",
34+ "/Users/simon-pierreboucher/firc0001_review/frames/frame_holdout.jsonl",
35+ "/Users/simon-pierreboucher/firc0001_review/frames/model_output_SEALED.jsonl",
36+ "/Users/simon-pierreboucher/firc0001_review/frames/weights.csv"
37+ ],
38+ "mde_gate_recall_diff_at_p0.05": 0.0781
39+}
\ No newline at end of file
added results/annotation_frame/design_productive_strata.csv +193 −0
@@ -0,0 +1,193 @@
1+item_group,run,trunc_bin,conf_bin,cat_group,N_population,n_sampled,weight
2+10-K md_and_a,june,shown0.25-0.9,conf>0.7,capex-type,10,10,1.0
3+10-K md_and_a,june,shown0.25-0.9,conf>0.7,ma_integration,30,10,3.0
4+10-K md_and_a,june,shown0.25-0.9,conf>0.7,other,5,5,1.0
5+10-K md_and_a,june,shown0.25-0.9,conf>0.7,technology-type,5,5,1.0
6+10-K md_and_a,june,shown<0.25,conf0.45-0.7,capex-type,4,4,1.0
7+10-K md_and_a,june,shown<0.25,conf0.45-0.7,ma_integration,2,2,1.0
8+10-K md_and_a,june,shown<0.25,conf0.45-0.7,other,2,2,1.0
9+10-K md_and_a,june,shown<0.25,conf>0.7,capex-type,471,10,47.1
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78+10-Q md_and_a,june,shown0.25-0.9,conf>0.7,ma_integration,46,10,4.6
79+10-Q md_and_a,june,shown0.25-0.9,conf>0.7,other,20,10,2.0
80+10-Q md_and_a,june,shown0.25-0.9,conf>0.7,technology-type,6,6,1.0
81+10-Q md_and_a,june,shown<0.25,conf0.45-0.7,capex-type,2,2,1.0
82+10-Q md_and_a,june,shown<0.25,conf0.45-0.7,ma_integration,6,6,1.0
83+10-Q md_and_a,june,shown<0.25,conf0.45-0.7,technology-type,1,1,1.0
84+10-Q md_and_a,june,shown<0.25,conf>0.7,capex-type,337,10,33.7
85+10-Q md_and_a,june,shown<0.25,conf>0.7,ma_integration,873,10,87.3
86+10-Q md_and_a,june,shown<0.25,conf>0.7,other,151,10,15.1
87+10-Q md_and_a,june,shown<0.25,conf>0.7,technology-type,85,10,8.5
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91+10-Q md_and_a,october,shown0.25-0.9,conf0.45-0.7,ma_integration,4,4,1.0
92+10-Q md_and_a,october,shown0.25-0.9,conf0.45-0.7,other,5,5,1.0
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94+10-Q md_and_a,october,shown0.25-0.9,conf>0.7,ma_integration,1984,10,198.4
95+10-Q md_and_a,october,shown0.25-0.9,conf>0.7,other,786,10,78.6
96+10-Q md_and_a,october,shown0.25-0.9,conf>0.7,technology-type,202,10,20.2
97+10-Q md_and_a,october,shown<0.25,conf0.45-0.7,capex-type,20,10,2.0
98+10-Q md_and_a,october,shown<0.25,conf0.45-0.7,ma_integration,45,10,4.5
99+10-Q md_and_a,october,shown<0.25,conf0.45-0.7,other,59,10,5.9
100+10-Q md_and_a,october,shown<0.25,conf0.45-0.7,technology-type,2,2,1.0
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102+10-Q md_and_a,october,shown<0.25,conf>0.7,ma_integration,17955,41,437.9268292682927
103+10-Q md_and_a,october,shown<0.25,conf>0.7,other,7471,17,439.47058823529414
104+10-Q md_and_a,october,shown<0.25,conf>0.7,technology-type,2025,10,202.5
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107+10-Q md_and_a,october,shown>=0.9,conf>0.7,capex-type,161,10,16.1
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109+10-Q md_and_a,october,shown>=0.9,conf>0.7,other,74,10,7.4
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128+10-Q other,october,shown0.25-0.9,conf0.45-0.7,ma_integration,11,10,1.1
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131+10-Q other,october,shown0.25-0.9,conf>0.7,ma_integration,1102,10,110.2
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134+10-Q other,october,shown<0.25,conf0.45-0.7,capex-type,18,10,1.8
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136+10-Q other,october,shown<0.25,conf0.45-0.7,other,36,10,3.6
137+10-Q other,october,shown<0.25,conf0.45-0.7,technology-type,3,3,1.0
138+10-Q other,october,shown<0.25,conf>0.7,capex-type,1628,10,162.8
139+10-Q other,october,shown<0.25,conf>0.7,ma_integration,13740,31,443.2258064516129
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141+10-Q other,october,shown<0.25,conf>0.7,technology-type,302,10,30.2
142+10-Q other,october,shown>=0.9,conf0.45-0.7,capex-type,22,10,2.2
143+10-Q other,october,shown>=0.9,conf0.45-0.7,ma_integration,17,10,1.7
144+10-Q other,october,shown>=0.9,conf0.45-0.7,other,16,10,1.6
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147+10-Q other,october,shown>=0.9,conf>0.7,ma_integration,7272,17,427.7647058823529
148+10-Q other,october,shown>=0.9,conf>0.7,other,2196,10,219.6
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154+8-K,june,shown0.25-0.9,conf>0.7,capex-type,287,10,28.7
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157+8-K,june,shown0.25-0.9,conf>0.7,technology-type,27,10,2.7
158+8-K,june,shown<0.25,conf0.45-0.7,capex-type,1,1,1.0
159+8-K,june,shown<0.25,conf0.45-0.7,ma_integration,1,1,1.0
160+8-K,june,shown<0.25,conf>0.7,capex-type,55,10,5.5
161+8-K,june,shown<0.25,conf>0.7,ma_integration,593,10,59.3
162+8-K,june,shown<0.25,conf>0.7,other,50,10,5.0
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170+8-K,june,shown>=0.9,conf>0.7,other,1671,10,167.1
171+8-K,june,shown>=0.9,conf>0.7,technology-type,207,10,20.7
172+8-K,october,shown0.25-0.9,conf0.45-0.7,capex-type,2,2,1.0
173+8-K,october,shown0.25-0.9,conf0.45-0.7,ma_integration,48,10,4.8
174+8-K,october,shown0.25-0.9,conf0.45-0.7,technology-type,1,1,1.0
175+8-K,october,shown0.25-0.9,conf>0.7,capex-type,614,10,61.4
176+8-K,october,shown0.25-0.9,conf>0.7,ma_integration,5598,13,430.61538461538464
177+8-K,october,shown0.25-0.9,conf>0.7,other,838,10,83.8
178+8-K,october,shown0.25-0.9,conf>0.7,technology-type,87,10,8.7
179+8-K,october,shown<0.25,conf0.45-0.7,capex-type,3,3,1.0
180+8-K,october,shown<0.25,conf0.45-0.7,ma_integration,8,8,1.0
181+8-K,october,shown<0.25,conf0.45-0.7,other,7,7,1.0
182+8-K,october,shown<0.25,conf>0.7,capex-type,228,10,22.8
183+8-K,october,shown<0.25,conf>0.7,ma_integration,1665,10,166.5
184+8-K,october,shown<0.25,conf>0.7,other,213,10,21.3
185+8-K,october,shown<0.25,conf>0.7,technology-type,26,10,2.6
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188+8-K,october,shown>=0.9,conf0.45-0.7,other,173,10,17.3
189+8-K,october,shown>=0.9,conf0.45-0.7,technology-type,17,10,1.7
190+8-K,october,shown>=0.9,conf>0.7,capex-type,7423,17,436.6470588235294
191+8-K,october,shown>=0.9,conf>0.7,ma_integration,20583,47,437.93617021276594
192+8-K,october,shown>=0.9,conf>0.7,other,8026,18,445.8888888888889
193+8-K,october,shown>=0.9,conf>0.7,technology-type,644,10,64.4
added results/annotation_frame/frame_composition.csv +221 −0
@@ -0,0 +1,221 @@
1+frame,stratum,n,weighted_n
2+productive,10-K md_and_a|june|shown0.25-0.9|conf>0.7|capex-type,10,10.0
3+productive,10-K md_and_a|june|shown0.25-0.9|conf>0.7|ma_integration,10,30.0
4+productive,10-K md_and_a|june|shown0.25-0.9|conf>0.7|other,5,5.0
5+productive,10-K md_and_a|june|shown0.25-0.9|conf>0.7|technology-type,5,5.0
6+productive,10-K md_and_a|june|shown<0.25|conf0.45-0.7|capex-type,4,4.0
7+productive,10-K md_and_a|june|shown<0.25|conf0.45-0.7|ma_integration,2,2.0
8+productive,10-K md_and_a|june|shown<0.25|conf0.45-0.7|other,2,2.0
9+productive,10-K md_and_a|june|shown<0.25|conf>0.7|capex-type,10,471.0
10+productive,10-K md_and_a|june|shown<0.25|conf>0.7|ma_integration,10,1502.0
11+productive,10-K md_and_a|june|shown<0.25|conf>0.7|other,10,275.0
12+productive,10-K md_and_a|june|shown<0.25|conf>0.7|technology-type,10,167.0
13+productive,10-K md_and_a|june|shown>=0.9|conf>0.7|capex-type,10,14.0
14+productive,10-K md_and_a|june|shown>=0.9|conf>0.7|ma_integration,10,11.0
15+productive,10-K md_and_a|june|shown>=0.9|conf>0.7|other,4,4.0
16+productive,10-K md_and_a|june|shown>=0.9|conf>0.7|technology-type,3,3.0
17+productive,10-K md_and_a|october|shown0.25-0.9|conf0.45-0.7|other,2,2.0
18+productive,10-K md_and_a|october|shown0.25-0.9|conf>0.7|capex-type,10,88.0
19+productive,10-K md_and_a|october|shown0.25-0.9|conf>0.7|ma_integration,10,174.0
20+productive,10-K md_and_a|october|shown0.25-0.9|conf>0.7|other,10,63.0
21+productive,10-K md_and_a|october|shown0.25-0.9|conf>0.7|technology-type,10,19.0
22+productive,10-K md_and_a|october|shown<0.25|conf0.45-0.7|capex-type,10,10.0
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added results/annotation_frame/frame_dev.csv +19368 −0
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