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 12 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
Responsible Use of Artificial Intelligence
Diane Gutiw, Jens M. Sorg, G. Rodríguez
2024· book-chapter· en· Advances in business information systems and analytics book series· Social Sciences
machine prediction:candidate · stsconsensus · none
3
citations
afffundunlabeled
Envisioning Ethical Mass Influence Systems
Alex Mayhew, Yimin Chen, Sarah Cornwell, S. Delellis Nicole, Dominique Kelly, Yifan Liu +1 more
2022· article· en· Proceedings of the Association for Information Science and Technology· Social Sciences
machine prediction:candidate · noneconsensus · none
3
citations
afffundgemma · stsgpt · stsmodels agree
Tech Ethics Through Trust Auditing
Matthew Grellette
2022· article· en· Science and Engineering Ethics· Social Sciences
machine prediction:candidate · stsconsensus · none
3
citations
affunlabeled
Humans “in the Loop”?
Nandita Biswas Mellamphy
2021· article· en· Nature and Culture· Social Sciences
machine prediction:candidate · stsconsensus · none
3
citations
affunlabeled
Autonomous Writing Futures
Ann Hill Duin, Isabel Pedersen
2021· book-chapter· en· Studies in computational intelligence· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Ethical Aspects of Information Literacy in Artificial Intelligence
Selma Letícia Capinzaiki Ottonicar, Ilídio Lobato Ernesto Manhique, Elaine Mosconi
2020· book-chapter· en· Advances in human and social aspects of technology book series· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Ethics for Nerds
Kevin Baum, Sarah Sterz
2022· article· en· The International Review of Information Ethics· Social Sciences
machine prediction:candidate · stsconsensus · none
2
citations

How this was built: Screen · Findings · About