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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.

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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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Ethics and Social Impacts of AI
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
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venuejournal
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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 ·
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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.
1,449 works in the cohort · of 4,299,418page 27 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.

aboutno affunlabeled
Integrating GenAI in Schools
2025· article· en· Zenodo (CERN European Organization for Nuclear Research)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
SL-Bots
Jeremy Turner, Michael Nixon, Jim Bizzocchi
2015· book-chapter· en· Advances in social networking and online communities book series· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
References
2023· book-chapter· en· Cambridge University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
D5.6 First report on the evaluation process
Charles Fage, Élise Durnerin, Joseph Gardette, Aline Roc, Enola Constanceau
2025· article· Zenodo (CERN European Organization for Nuclear Research)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Safe AI Doctrine
2025· preprint· en· Zenodo (CERN European Organization for Nuclear Research)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
The Big Picture
2023· book-chapter· en· Cambridge University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Engagement
Haroon Sheikh, J.E.J. Prins, Erik Schrijvers
2023· book-chapter· en· Research for policy· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Global Regulatory Landscape
Chrisella Natasia Tanujaya, Binastya Anggara Sekti
2025· book-chapter· Advances in computational intelligence and robotics book series· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Agents in the World
2023· book-chapter· en· Cambridge University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Conclusion
Stefan-Michael Wedenig
2025· book-chapter· en· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affaboutunlabeled
Curiosity to Confidence with the AI Hub
Victoria Chen, Ashnaa Narumathan, Siobhan O'Donoghue
2025· article· en· Journal of innovation in polytechnic education.· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Why and how to evaluate Trustworthiness in AI?
Élise Durnerin, Joseph Gardette, Enola Constanceau, Aline Roc, Charles Fage
2025· article· Zenodo (CERN European Organization for Nuclear Research)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Fairness in social machines: a systematic review
Mir Saeed Damadi, Alan Davoust
2025· article· en· Journal of Information Communication and Ethics in Society· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Ethics: To Do or Not To Do?
Cheryl Trepanier, Ali Shiri, Toni Samek
2018· article· en· Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Searching for Solutions
2023· book-chapter· en· Cambridge University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
E-Governance and Ethical AI
C. S. Raghuvanshi, Rashi Shukla, Subhit Shukla
2025· book-chapter· en· IGI Global eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Charting a faceted categorization of AI and ethics
Toni Samek, Ali Shiri
2022· article· en· Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Causality
2023· book-chapter· en· Cambridge University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
D5.9 First report on the evalaution process
Charles Fage, Élise Durnerin, Joseph Gardette, Aline Roc, Enola Constanceau
2025· article· Zenodo (CERN European Organization for Nuclear Research)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
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