Findings

508,744 of 793,883 works (64%) in the metaresearch topic space have no raw affiliation strings in OpenAlex.

Of 4,516 OpenAlex topics, 0 name metaresearch as a field. The 11 topics that do carry metaresearch content are scattered across 7 different OpenAlex fields.

The single term reproducibility retrieves 43,392 Canadian works, of which only 0.8% fall in the metaresearch topic space. Keyword retrieval cannot separate the metaresearch sense from the everyday one.

French is 2.7% (395/14,873) of Canadian metaresearch in OpenAlex. A dedicated French lexicon finds 168 Canadian works, against 8,026 in English.

Erudit matches 0 sources in OpenAlex, but its OAI-PMH endpoint is live and exposes 379 harvestable sets.

Naive two-route capture-recapture estimates 467,541 Canadian metaresearch works, implying Canada produces 59% of the world's metaresearch against an observed 1.9%. The estimator is void here; we cut it rather than dress it up as a lower bound.

Three frontier models (Opus 4.8, GPT-5.6 high, Grok 4.5) screened the same 1,000 works from the REAL 4.3M frame, on the rubric's FULL eight-field payload, with randomized manifest-logged chunks and harness-written labels: every defect D1, D2, D11 and finding 16 identified, repaired. Design-weighted base rates span 1.89% to 3.54% (1.9x). But the sets are the finding, as finding 16 predicted: of the 51 works ANY model called metaresearch, only 19 (37%) were called metaresearch by ALL THREE, and 24 (47%) rest on a single model's opinion. THE FIELD'S BOUNDARY IS NOT A LINE THE MODELS SHARE; IT IS A REGION THEY EACH CUT DIFFERENTLY. GPT-5.6 also violated the locked output schema on 18 of 1,000 records, writing genre values into the tier field, which the manifest validator caught. The deliverable is not the base rate: it is the disagreement dossier, the 51 works that mark the empirical boundary of the field and against which the inclusion criteria must actually be written.

Swap which model is called 'the screener' and the base rate moves from 1.06% to 2.37%: a 2.2x spread, from 37,032 to 83,022 works in the frame. The two screeners agree on in/out for 98.4% of the frame (design-weighted), but that figure is dominated by the settled rejects: agreement falls to 95% inside the contested boundary. THE SCREENER-SWAP RANGE, NOT THE BINOMIAL CI ON EITHER MODEL ALONE, IS THE HONEST UNCERTAINTY ON THE FIELD'S SIZE.

Screening 5,737 unfiltered Canadian works against the rubric puts metaresearch at 1.31% of Canadian research, implying ~45,850 works in the 3.5M-work frame, sizing the field without a search strategy at all. The 95% CI on this screener's labels is 1.03-1.64%, but that is sampling error, NOT the uncertainty: swap the screener and the estimate lands outside it (finding 10). The field is somewhere between 37,000 and 83,000 works, and only the human audit can narrow that.

The base rate's real bias is not recency but MISSING ABSTRACTS: 31.5% of the partition has none, and the screen finds 0.78% metaresearch there against 1.55% where an abstract exists (chi-square p = 0.023, robust to adjustment for year and language). A third of the frame is screened on its title alone. What this does NOT show, and an earlier draft wrongly claimed, is that the blindness is DIFFERENTIAL by tradition: the T2 no-abstract cell holds 4 works and the interaction is not significant (p = 0.141). That claim is withdrawn, as is 'Erudit's exact profile' (the stratum is 99% English and the works are NEWER, not older). The main effect is the finding.

Two limits on the machine labels, found by attacking the fixes. (A) The topic route's recall is 12% against screener A and 7% against screener B: finding 14's instrument (ii) scores filters against MACHINE labels, so it measures agreement with a machine, not accuracy, and finding 10 already showed that swings by a factor of two. The conclusion strengthens (the second screener thinks the route is WORSE) but 12% is not truth. (B) The pilot holds exactly 1 French in-scope work, so seeing 20 French positives needs ~1,620 coded French records against an audit budget of 1,000. FRENCH SENSITIVITY IS NOT ESTIMABLE IN AN OPENALEX-ONLY FRAME. That is an argument for the Erudit harvest, not against the French claim, but the pilot did not run that harvest, so the power is stated as a condition rather than a promise.

Haiku, the model finding 13 budgets the entire screen on, lands near Sonnet's base rate (1.27% vs 1.06%) and agrees with it on 98.1% of the frame, but their in-scope SETS overlap 16% unweighted and 10% design-weighted (Sonnet-GPT: 54%/37%); of Sonnet's 58 positives Haiku agrees on 12. RATE AGREEMENT IS NOT SET AGREEMENT. Worse: agents of the SAME model on the SAME prompt disagree beyond chance in BOTH arms after conditioning on stratum (CMH p = 0.0056 and 0.015), with raw spreads 3.1x and 5.2x against 2.2x between models, and the agents' ordering FLIPS between arms. The noise inside one model is at least the size of the difference between models, and the pilot's own 40-agent screen is too underpowered to rule the same instability out (5 of 37 chunks found zero metaresearch; p = 0.113).

The screen's largest measured bias is the abstract gap: 23.3% of the frame (1,003,117 works) has NO ABSTRACT, and finding 11 showed the screen finds HALF as much metaresearch there. Cascading PubMed, Europe PMC and Crossref recovers 37.8% of a 500-work sample, cutting title-only exposure to ~14.5% of the frame. But I BUILT THE CASCADE AROUND CROSSREF as the discipline-agnostic rescue, and it recovered 2 abstracts against PubMed's 180: publishers do not deposit them, so THAT RESCUE DOES NOT EXIST (D15). The gap is therefore not a metadata failure a better index fixes; it is STRUCTURAL. Recovery is 91.2% for reviews against 6.2% for book chapters, 38.8% English against 15.4% French. So the tempting shortcut, 'just screen the works that have abstracts', is a SELECTION ON A COVARIATE THAT PREDICTS THE OUTCOME which would delete 61.6% of book chapters against 22.1% of articles, AND the works it deletes are exactly the works no cascade can rescue. Defensible only as a DECLARED exclusion with a measured cost, and the audit keeps a sampling floor in it.

Joined to the Canadian frame by DOI, Retraction Watch records 143 works that OpenAlex does NOT flag as retracted, including 49 outright retractions. But the undercount is the smaller problem. 52 of these carry an EXPRESSION OF CONCERN, and OpenAlex HAS NO FIELD FOR ONE: `is_retracted` is a boolean over a state space with at least four values (retraction, expression of concern, correction, reinstatement), so it can express one and silently reports the rest as FALSE, which reads as 'fine'. Nor can a boolean carry WHY. This is finding 1's disease in a second schema: the canonical database cannot express the distinction the field turns on.

Scored against the rubric, the topic route retrieves 12% of Canadian metaresearch (95% CI 5.6-21.6%) at 60% precision: it misses 66 of 75. It fails because OpenAlex files a work by what it is about, and metaresearch about cardiology reads as cardiology: the field is invisible to topic retrieval precisely because it is about other fields.

The PRIMARY ESTIMAND leans on CA-FUND to rescue works whose affiliation is missing (finding 2: 64% have no affiliation string), and nothing had tested it. Tested against CIHR's own database of 44,190 funded projects, the result REFUTED THE HYPOTHESIS I WROTE BEFORE RUNNING IT: OpenAlex tags 178,133 frame works with CIHR, or 4.03 per grant, a plausible rate showing no under-tagging (DEVIATIONS.md D14). What the data DOES support needs no hypothesis of mine: 71.2% OF THE FRAME CARRIES NO FUNDER METADATA AT ALL, which is CA-FUND's hard ceiling, and 65.9% of Canadian-AFFILIATED works carry none either. Both clauses of the estimand rest on metadata that is mostly absent, which is why the frame is a union of four routes and why the audit must sample the works no route reached.

Affiliation finds 14,873 works; a further 3,964 are about Canada with no Canadian affiliation. NSERC has 9.2x SSHRC's linked works, so funder-based rules under-count the social sciences.

The registry is the only REFERENCE STANDARD in this project not made of machine labels: ClinicalTrials.gov knows a Canadian trial happened independently of any pipeline, so it cannot be wrong in the pipeline's favour. Of 304 publications SPONSORS THEMSELVES reported as results of completed Canadian-located trials, the frame holds 160: a naive recall of 52.6%, which fell so close to the 44.5% THIS PROPOSAL OPENS WITH that it read as a replication. IT IS AN ARTIFACT, and running the disambiguation is the only thing that caught it: 137 of the 145 'misses' have NO CANADIAN AUTHOR (multi-site international trials with a Canadian SITE), and a frame of Canadian RESEARCH is CORRECT to exclude them. A trial with a Canadian site is not a publication with a Canadian author. Against the population the frame actually claims, recall is 95.2% (95% CI 90.8-97.9), and the real defect is 8 works OpenAlex holds WITH a Canadian author that the frame's own routes still missed. The frame is GOOD at this, the dramatic parallel was a coincidence between two different populations, and I had every incentive not to check. DEVIATIONS.md D16.

The frame carries 156,086 preprints, and the tempting move was to assert that OpenAlex covers the preprint servers and skip the ingest. That is a COVERAGE CLAIM, and this project does not get to make one from inside the pipeline being claimed for: it is the topic route certifying its own recall (finding 12). Measured instead against the servers' OWN API, OpenAlex indexes 99.6% of the 705 bioRxiv and medRxiv preprints enumerated (3 missing). The claim survives measurement, so no separate preprint ingest is built.

The audit as first specified could not measure what it exists to measure. A simple random sample of the 3,461,261-record screened-out mass yields an expected 0.4 hits from the 600 records budgeted; seeing 20 would take 2,009 coder-hours against the 65 planned. Score-stratified oversampling rescues it only partly (the pilot shows 30 of 37 disputed works sit at the contested boundary), and it stays BLIND to the confident rejects. Which works those are is a MECHANISM, not a measured differential: the rubric says judge on the title alone with no abstract, and says T2 work may use none of the field's vocabulary, so a work with neither is rejected confidently and never sampled. (An earlier version cited a 3.6x figure here as if it were measured; it rested on four works and is withdrawn.) So recall is measured two other ways: against the 5,737 works that already carry rubric labels, and by KNOWN-ITEM recall on a venue reference set, because venue is an external criterion immune to the aboutness that defeats everything else.

The full rubric over the 3,507,205-work frame with two screeners costs $5,357, not the $24,968 an earlier version of this script reported: the rubric is a system prompt sent once per CALL, and the pilot's own chunks batch 155 works per call, so it was overcharged 4.7x. That error was not cosmetic. It made me propose a cheap TRIAGE in front of the screen, which is a RETRIEVAL STEP in a project whose central finding is that retrieval destroys these maps. With the arithmetic right the triage is unnecessary: the full rubric over EVERY work in the frame, plus a second screener on a 20,000-record sample, costs $908 and leaves ~$1,992 for the human coder. THE PREFILTER IS DELETED.

The OpenAlex API is metered (1,000 credits per ~11h; $0.10 free tier). Enumerating the 3,507,205-work Canadian frame needs 17,537 cursor-paged calls: 8.2 days per pass on the free tier. An API-based pipeline at this scale is neither free nor reproducible; the pinned snapshot is both.