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
Proceedings of the Association for Information Science and Technology
Topic
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.

252 results · 1 filter active ·
Results by year
20152025
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.
252 works in the cohort · of 4,299,418page 5 of 6

Labels cover 3 of 252 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 252 of 252 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
Supporting student agency with transmedia
Amanda Hovious, Valerie Harlow Shinas, Ian Harper
2018· article· en· Proceedings of the Association for Information Science and Technology· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Storytelling for Translational Research Impact
Sarah Gonzalez, Ying‐Hsang Liu, Sue Yeon Syn, Stephann Makri, Lynn Silipigni Connaway, Lisa M. Given +2 more
2023· article· en· Proceedings of the Association for Information Science and Technology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
0
citations
afffundunlabeled
(How) should information world maps be visually analyzed?
Devon Greyson, Heather O’Brien, Saguna Shankar
2018· article· en· Proceedings of the Association for Information Science and Technology· Social Sciences
machine prediction:candidate · metaresearchconsensus · none
0
citations
afffundunlabeled
Health Misinformation Research
Devon Greyson, Morgan Lundy, Sophie Rutter, David Roger Walugembe
2024· article· en· Proceedings of the Association for Information Science and Technology· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Developing community phenotype ontologies: Understanding users' preferences
Limin Zhang, Zuleima Cota, Hong Cui, Hsin‐Liang Chen, Bruce A. Ford, Joel Sach +4 more
2019· article· en· Proceedings of the Association for Information Science and Technology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Post‐checkout (Non‐)usage of Library Digital Content
Angela Lieu, Dangzhi Zhao
2018· article· en· Proceedings of the Association for Information Science and Technology· Social Sciences
machine prediction:candidate · scholarly_communicationconsensus · none
0
citations
affunlabeled
Queer Data
Patrick Keilty, Marika Cifor, Ben Watson, Andrew Weibe
2024· article· en· Proceedings of the Association for Information Science and Technology· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Amplifying chance for positive action and serendipity by design
Sarah A. Buchanan, Sabrina Sauer, Anabel Quan‐Haase, Naresh Kumar Agarwal, Sanda Erdelez
2020· article· en· Proceedings of the Association for Information Science and Technology· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About