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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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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.

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

37,702 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
37,702 works in the cohort · of 4,299,418page 338 of 755

Labels cover 42 of 37,702 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 37,702 of 37,702 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
Merging Corporatized Financial Markets
Kobana Abukari, Isaac Otchere
2016· article· en· SSRN Electronic Journal· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Does Political Diversity Inhibit Blood Donations?
Krzysztof Pelc, Sung Eun Kim
2024· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
A Genealogical Approach to Algorithmic Bias
Marta Ziosi, David Watson, Luciano Floridi
2024· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · stsconsensus · none
1
citations
affno abstractunlabeled
Optimism, Net Worth Trap, and Asset Returns
Goutham Gopalakrishna, Seung Joo Lee, Theofanis Papamichalis
2024· preprint· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Why Do Children Care More About Animals than Adults Do?
Lucius Caviola, Matti Wilks, Claudia Suárez Yera, Carter Allen, Guy Kahane, Luke McGuire +5 more
2024· preprint· en· SSRN Electronic Journal· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Unemployment Insurance and Macro-Financial (In)Stability
Gazi Kabaş, Yavuz Arslan, Ahmet Değerli, Burhanettin Kuruşçu, Bülent Güler
2024· article· en· SSRN Electronic Journal· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
State of Open Banking in India and UK
Ahmed Khan, Victor Murinde, Rosemarie Mcgarrell, Thankom Arun, Varnika Goel, Philip Kostov +2 more
2024· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Dynamic Incentives and Retirement
Florin Şabac
2007· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Hedge Funds are on the Ball When Insiders Trade
Pouyan Foroughi, Jerry T. Parwada, Yixuan Rui, Jianfeng Shen
2023· preprint· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Trade Policy Under the Biden Administration
Simon Lester
2020· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About