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
Topic
Auction Theory and Applications
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.

1,300 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.
1,300 works in the cohort · of 4,299,418page 9 of 26

Labels cover 1 of 1,300 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 1,300 of 1,300 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
Competing through information provision
Jean Guillaume Forand
2013· article· en· International Journal of Industrial Organization· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Optimal Procurement with Quality Concerns
Giuseppe Lopomo, Nicola Persico, Alessandro T. Villa
2023· article· en· American Economic Review· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affno abstractunlabeled
Information Asymmetries in Pay-Per-Bid Auctions
John W. Byers, Michael Mitzenmacher, Georgios Zervas
2010· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Revenue from matching platforms
Philip Marx, James Schummer
2021· article· en· Theoretical Economics· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
Group Role Assignment With Minimized Agent Conflicts
Qian Jiang, Dongning Liu, Haibin Zhu, Baoying Huang, Naiqi Wu, Yan Qiao
2024· article· en· IEEE Transactions on Systems Man and Cybernetics Systems· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Investment and Information Acquisition
Dimitri Migrow, Sergei Severinov
2022· article· en· American Economic Journal Microeconomics· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Contracting in Vague Environments
Marie-Louisew Vierø
2012· article· en· American Economic Journal Microeconomics· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Algorithmic Trading with Learning
Álvaro Cartea, Sebastian Jaimungal, Damir Kinzebulatov
2013· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Approximately Revenue-Maximizing Auctions for Deliberative Agents
L. Elisa Celis, Anna R. Karlin, Kevin Leyton‐Brown, Chương V. Nguyen, David W. Thompson
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
afffundno abstractunlabeled
Bidding collusion without passive updating
Charles Z. Zheng
2019· article· en· Journal of Mathematical Economics· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Human Judgment and AI Pricing
Ajay Agrawal, Joshua S. Gans, Avi Goldfarb
2018· preprint· en· National Bureau of Economic Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About