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 20 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Noisy Signaling in Monopoly
Leonard J. Mirman, Egas M. Salgueiro, Marc Santugini
2013· preprint· en· RePEc: Research Papers in Economics· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · none
1
citations
affunlabeled
Predatory Bidding in Sequential Auctions
Hikmet Günay, Xin Meng
2007· article· en· SSRN Electronic Journal· Decision Sciences
distilled prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Detecting Collusion in Timber Auctions : An Application to Romania
Jean‐Daniel Saphores, Jeffrey R. Vincent, Valy Marochko, I. V. Abrudan, Laura Bouriaud, Clifford Zinnes
2012· preprint· en· RePEc: Research Papers in Economics· Decision Sciences
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
affunlabeled
Security and Trust of Online Auction Systems in E-Commerce
Pouwan Lei, Lih-Jiun Lo, C.R. Chatwin, Robert C. Young, Malcolm I. Heywood, A. Nur Zincir‐Heywood
2003· book-chapter· en· IGI Global eBooks· Decision Sciences
distilled prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
(Bad) reputation in relational contracting
Rahul Deb, Matthew Mitchell, Mallesh M. Pai
2022· preprint· en· Theoretical Economics· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · none
1
citations
affunlabeled
Honesty and Informal Agreements
Martin Dufwenberg, Maroš Servátka, Radovan Vadovič
2015· preprint· en· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
1
citations
afffundunlabeled
Multi-item Auctions for Automatic Negotiation
Houssein Ben-Ameur, Brahim Chaib-draa, Peter Kropf
2002· preprint· en· RePEc: Research Papers in Economics· Decision Sciences
distilled prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
An Open Access Market for Global Communications
Peter Cramton, Erik Bohlin, Simon Brandkamp, Jason Dark, Darrell Hoy, Albert S. Kyle +3 more
2024· preprint· en· SSRN Electronic Journal· Decision Sciences
distilled prediction:candidate · scholarly_communication+open_science+research_integrityconsensus · open_science
1
citations
venueno affunlabeled
Dynamic local interaction model. Modèle et algorithms
Arnaud Canu, Abdel‐Illah Mouaddib, F. Poulet
2012· article· fr· Revue d intelligence artificielle· Decision Sciences
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Data and AI Markets in a Nutshell
Jian Pei, Xiaohui Yu
2025· article· en· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Gains to bidder firms revisited
B. Espen Eckbo, Karin S. Thorburn
2012· article· en· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Under ε-Best Response
2011· article· en· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
afffundunlabeled
Trustworthy ML Regulation as a Principal-Agent Problem
Mohammad Yaghini, Andrew Magnuson, Natalie Dullerud, Nicolas Papernot
2025· article· en· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
PRINCIPAL AuTHOR
2012· article· en· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Rules Versus Mechanisms
Joshua S. Gans
2023· book-chapter· en· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations

How this was built: Screen · Findings · About