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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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Auction Theory and Applications
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
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 ·
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20002025
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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.
1,300 works in the cohort · of 4,299,418page 2 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
Selling options
Simon Board
2006· article· en· Journal of Economic Theory· Decision Sciences
machine prediction:candidate · noneconsensus · none
68
citations
affunlabeled
Costly Signalling in Auctions
Johannes Hörner, Nicolas Sahuguet
2007· article· en· The Review of Economic Studies· Decision Sciences
machine prediction:candidate · noneconsensus · none
65
citations
affunlabeled
Discretion in Hiring
Mitchell Hoffman, Lisa Kahn, Danielle Li
2015· report· en· National Bureau of Economic Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
57
citations
affno abstractunlabeled
Multi-agent learning for engineers
Shie Mannor, Jeff S. Shamma
2007· article· en· Artificial Intelligence· Decision Sciences
machine prediction:candidate · noneconsensus · none
52
citations
aboutno affunlabeled
High-Quality Policies for the Canadian Traveler's Problem
Patrick Eyerich, Thomas Keller, Malte Helmert
2010· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Decision Sciences
machine prediction:candidate · noneconsensus · none
51
citations
affno abstractunlabeled
Link bidding in laboratory networks
Cary Deck, Cathleen Johnson
2004· article· en· Review of Economic Design· Decision Sciences
machine prediction:candidate · noneconsensus · none
50
citations
affunlabeled
Auctions with Limited Commitment
Qingmin Liu, Konrad Mierendorff, Xianwen Shi, Weijie Zhong
2019· article· en· American Economic Review· Decision Sciences
machine prediction:candidate · noneconsensus · none
49
citations
affno abstractunlabeled
Competition between auctions
Ernan Haruvy, Peter T. L. Popkowski Leszczyc, James C. Cox, Eric Greenleaf, Wolfgang Jank, Sandy D. Jap +2 more
2008· article· en· Marketing Letters· Decision Sciences
machine prediction:candidate · noneconsensus · none
49
citations
affvenueunlabeled
Protection for sale with imperfect rent capturing
Giovanni Facchini, Johannes Van Biesebroeck, Gerald Willmann
2006· article· en· Canadian Journal of Economics/Revue canadienne d économique· Decision Sciences
machine prediction:candidate · noneconsensus · none
48
citations
affno abstractunlabeled
Organizational inertia and dynamic incentives
Marcel Boyer, Jacques Robert
2005· article· en· Journal of Economic Behavior & Organization· Decision Sciences
machine prediction:candidate · noneconsensus · none
47
citations

How this was built: Screen · Findings · About