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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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OECD science, technology and industry working papers
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Retraction
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
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aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

15 results · 1 filter active ·
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20012025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
15 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 15 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 15 of 15 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affunlabeled
Mapping Careers and Mobility of Doctorate Holders
Laudeline Auriol, Bernard Félix
2010· paratext· en· OECD science, technology and industry working papers· Social Sciences
machine prediction:candidate · metaresearchconsensus · none
106
citations
aboutno affunlabeled
Demand for AI skills in jobs
Mariagrazia Squicciarini, Heike Nachtigall
2021· paratext· en· OECD science, technology and industry working papers· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
69
citations
fundno affunlabeled
Biotechnology Statistics in OECD Member Countries
Brigitte Van Beuzekom
2001· paratext· en· OECD science, technology and industry working papers· Medicine
machine prediction:candidate · bibliometricsconsensus · none
23
citations
aboutno affunlabeled
Gene editing in an international context
Anu Shukla-Jones, Steffi Friedrichs, David E. Winickoff
2018· paratext· en· OECD science, technology and industry working papers· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
16
citations
aboutno affunlabeled
Identifying artificial intelligence actors using online data
Hélène Dernis, Flavio Calvino, Laurent Moussiegt, Daisuke Nawa, Lea Samek, Mariagrazia Squicciarini
2023· report· en· OECD science, technology and industry working papers· Social Sciences
machine prediction:candidate · noneconsensus · none
6
citations
aboutno affunlabeled
Venture Capital Policies in Canada
Günseli Baygan
2003· paratext· en· OECD science, technology and industry working papers· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About