| 1 |
Delete out/results.json, re-run analysis.py twice, cmp outputs |
PASS (byte-identical) |
| 2 |
n_supported + n_not_supported + n_not_assessable = 6 = n_analysed = len(assertions.json) |
PASS (2+3+1=6) |
| 3 |
Extract SHA-256 equal README values |
PASS (both) |
| 4 |
n_input_rows equal wc -l (14, 134, 58) and JSON item count (6) |
PASS |
| 5 |
A1: 100(exp(-0.431)-1) = -35.01% vs printed -35.0%; |
t |
| 6 |
A3: 100(exp(-0.283)-1) = -24.65% vs printed -24.7% (source's own conversion consistent) |
PASS |
| 7 |
A4: CI ±1.96SE = [-0.896, 0.084], ±2SE = [-0.906, 0.094]; both contain 0; no stars printed on -0.406 |
PASS (consistent) |
| 8 |
A5: regex search for men/women/male/female/sex/gender in DCR extract returns 0 lines |
PASS |
| 9 |
A6: -1.99/17.3 = -11.50%; CI [-2.24,-1.75] contains -1.99 |
PASS |
| 10 |
Each locator has the required form; one per assertion |
PASS |
| 11 |
Source-internal consistency: A19 SE 0.218 vs Table 6 0.210; 640 vs 608 controls; abstract fatal 35% vs Table 7 -29.8% |
Inconsistencies FOUND in the source; reported in data_issues (not fixable by us) |
| 12 |
Baseline (naive number-presence) vs final verdicts: agree on 4/6; differs on A2 (sign ignored) and A5 (no numbers) — expected failure modes of the naive rule |
Recorded |