MétaCan
ENFR
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.

Export CSVExport JSONAPI query

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
IT Professional
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.

42 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.
42 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 42 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 42 of 42 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
The Next Step for Learning Analytics
Jinan Fiaidhi
2014· article· en· IT Professional· Computer Science
machine prediction:candidate · noneconsensus · none
38
citations
affunlabeled
MORF: A Mobile Health-Monitoring Platform
Rachid Benlamri, Luke Docksteader
2010· article· en· IT Professional· Computer Science
machine prediction:candidate · noneconsensus · none
36
citations
affunlabeled
Nothing Is Harder to Resist Than the Temptation of AI
Andrew Park, Jan Kietzmann, Jayson Killoran, Yuanyuan Cui, Patrick van Esch, Amir Dabirian
2023· article· en· IT Professional· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Electronic Commerce Meets the Semantic Web
Jelena Jovanović, Ebrahim Bagheri
2016· article· en· IT Professional· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Ethics in the Digital Era: Nothing New?
Laura Specker Sullivan, Peter B. Reiner
2020· article· en· IT Professional· Social Sciences
machine prediction:candidate · noneconsensus · none
5
citations
aboutno affunlabeled
Mainframes, Cobol still popular
L.D. Paulson
2001· article· en· IT Professional· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Implementing Behavioral Biometrics With TRUST
Jayson Killoran, Yuanyuan Cui, Andrew Park, Patrick van Esch, Amir Dabirian, Jan Kietzmann
2023· article· en· IT Professional· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Cognitive Gaming
Wei Cai, Yuanfang Chi, Victor C. M. Leung
2017· article· en· IT Professional· Psychology
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Vulnerability Assessment of Learning Management Systems
Elio Sancristobal, Rafael Vargas, Rosario Gil, Russ Meier, Hamadou Saliah-Hassane, Manuel Castro
2023· article· en· IT Professional· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
The Blockchain for Personalized Medicine
Jeandri Robertson, Alexander Hedlund, Jan Kietzmann, Christine Pitt, Amir Dabirian
2025· article· en· IT Professional· Environmental Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Innovations Using Blockchain–Part 2
Jeffrey Voas, Jinan Fiaidhi
2019· article· en· IT Professional· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
2015 Editorial Calendar
Richard Kühn, Tim Weil, Robert R. Harmon, Irena Bojanova, George Hurlburt, Stem Corp +7 more
2014· article· en· IT Professional· Engineering
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Innovations Using Blockchain–Part 1
Jeffrey Voas, Jinan Fiaidhi
2019· article· en· IT Professional· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affno abstractunlabeled
Masthead
2015· paratext· en· IT Professional
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About