MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
→
Sort
Language
Type
Field
Venue
SSRN Electronic Journal
Topic
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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
venuejournal
aboutaboutness

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 ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
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 573 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.

affno abstractunlabeled
Revisiting the CEO Effect Through a Machine Learning Lens
Hajime Shimao, Sung Joo Kim, Warut Khern-am-nuai, Maxime C. Cohen
2023· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Optimal Liquidity and Asset Bubbles
Jérôme Detemple, Yerkin Kitapbayev, Rodolfo Prieto
2023· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Making Trade Work for Climate
Alaz Munzur, Katharina Koch, Jennifer Winter
2025· preprint· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
First Nations People, Law and Injustice
Stephen Bottomley, Simon Bronitt
2023· article· en· SSRN Electronic Journal· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Optomechanics and Brain Biology
S. Suresh
2018· article· en· SSRN Electronic Journal· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Syndicated Credit Cut and Firm Performance
Pei Shao, Deming Wu
2014· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Equilibria and Efficiency in a Reinsurance Market
Michael B. Zhu, Mario Ghossoub, Tim J. Boonen
2022· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Induced Forests in Some Distance-Regular Graphs
Karen Gunderson, Karen Meagher, Joy Morris, Venkata Raghu Tej Pantangi
2023· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Dancing the Horah
Shauna Van Praagh
2013· article· en· SSRN Electronic Journal· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Do I Trust you or your Avatar? An Experimental Study
Vivianna Fang He, Daniel Landau, Ithai Stern, Bart Vanneste
2023· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Ukrainian Reparation Loan: How it Would Work
Hugo Dixon, Lee C. Buchheit, Daleep Singh
2024· article· en· SSRN Electronic Journal· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About