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
Cultural Insights and Digital Impacts
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.

6,036 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.
6,036 works in the cohort · of 4,299,418page 33 of 121

Labels cover 22 of 6,036 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 6,036 of 6,036 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
Test de DOI événementiel pour Espace temps
Jean-Robert Bisaillon
2019· dataset· fr· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
1. Le document au cœur de l'organisation muséale
Maryse Rizza, Corinne Barbant, Patrick Le Bœuf, Stéphanie Fargier-Demergès
2014· article· fr· Documentaliste-Sciences de l Information· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Loin des yeux
Claire Moeder
2016· book· fr· Optica eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
[no title]
Karla Kennedy-Hagan
2007· article· en· Journal of Nutrition Education and Behavior· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
fundno affunlabeled
Blogues de Sciences Francophones ?
Arthur Charpentier
2014· article· fr· OpenEdition (OpenEdition)· Computer Science
machine prediction:candidate · scholarly_communicationconsensus · none
0
citations
aboutno affunlabeled
Corridart, 25 ans plus tard = Corridart Revisited
Sandra Paikowsky, G.B. Brook, Kate Darley, Mélanie Dugas, Samantha Forbes Caldicott, Anne Kaye +4 more
2001· book· fr· Galerie d'art Leonard & Bina Ellen Art Gallery eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
Quelques cas de figure / A Few Case Studies
Jacques Doyon
2008· article· fr· Ciel variable : art, photo, médias, culture· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
ARTIFICIALISATION HUMAINE ET DENTAIRE
Frédéric-Gaël Theuriau
2018· article· fr· Revue CMC· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Emanuel Licha : Et maintenant regardez cette machine
Lesley Johnstone, Emanuel Licha, Volker Pantenburg, Susan Schuppli
2017· book· fr· Musée d'art contemporain de Montréal eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About