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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
fundfunder
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
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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 13 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.

affno abstractunlabeled
Ethical Foundations of AI with Personality
Gulnara Z. Karimova
2025· book-chapter· en· SpringerBriefs in computer science· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Burdens of Proposing
David Godden, Simon Wells
2022· article· en· Informal Logic· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
Deep learning as machine metis
Primož Krašovec
2025· article· en· AI & Society· Social Sciences
machine prediction:candidate · stsconsensus · none
2
citations
affno abstractunlabeled
This hot AI summer will impact Brazil’s democracy
Cristina Godoy Bernardo de Oliveira, Fábio Gagliardi Cozman, João Paulo Cândia Veiga
2023· article· en· Nature Human Behaviour· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Evaluating the Social Impact of Generative AI Systems
Irene Solaiman, Zeerak Talat, William S. Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett +23 more
2025· book-chapter· en· Oxford University Press eBooks· Social Sciences
machine prediction:candidate · stsconsensus · none
2
citations
affvenueunlabeled
Assessing Artificial Intelligence
Susan Wood
2020· article· en· Toronto Journal of Theology· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Designing an AI policy
Margaret Debelius, Molly Chehak, Kenny Le, Sophia Oh, Sarah Lyons, Grace Kim +2 more
2025· article· en· International Journal for Students as Partners· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Why Does AI Companionship Go Wrong?
Ziwei Gao
2024· article· en· The International Review of Information Ethics· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Ethical Design in an AI-Driven World
Diana Olynick
2024· book-chapter· en· Design Thinking· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Humane Driving
Vaughan Black, Andrew Fenton
2021· article· en· Canadian Journal of Law & Jurisprudence· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
fundno affunlabeled
Case Study: HarassMap
Cameron Neylon
2017· article· en· Research Ideas and Outcomes· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
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