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
Ethics and Social Impacts of AI
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.

1,449 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.
1,449 works in the cohort · of 4,299,418page 23 of 29

Labels cover 12 of 1,449 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 1,449 of 1,449 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
Feelings, Values, Ethics and Skills
Stephan Petrina
2007· book-chapter· en· IGI Global eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
AI in Healthcare: Navigating Legal Risk Assessment with JusticeBot
Sébastien Meeùs, Valentina Dalla Giovanna, Samyar Janatian, Hannes Westermann, Karim Benyekhlef, Grégory Lewkowicz
2024· book-chapter· en· Frontiers in artificial intelligence and applications· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Controlling Smart Technology
John Murray, John Rushby, Daniel Sánchez
2022· article· en· The International Review of Information Ethics· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
A Rossian Method for Applying Principles in AI
Peter Andes, Robin Lau, Geoffrey Rockwell, Tammy S. Mah
2024· article· en· The International Review of Information Ethics· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Unpacking AI Security Considerations
Namosha Veerasamy, Danielle Badenhorst, Mazwi Ntshangase, Errol Baloyi, Nokuthaba Siphambili, Oyena Mahlasela
2024· article· en· International Conference on Cyber Warfare and Security· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Software Fairness Testing in Practice
Ronnie de Souza Santos, Matheus de Morais Leça, Reydne Santos, Cleyton Magalhães
2025· article· Social Sciences
machine prediction:candidate · metaresearchconsensus · none
0
citations
affunlabeled
Três faces do ChatGPT
Juliana Michelli S. Oliveira, Rodrigo de Almeida Siqueira, Rogério de Almeida
2023· article· pt· SCIAS Educação Comunicação e Tecnologia· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About