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 23 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.

affunlabeled
Archives et République
Philippe Bélaval
2001· article· fr· Le Débat· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Mythes écologiques du numérique
Ambroise Baillifard, Olivier Ertz, Stéphane Lecorney, Corinna Martarelli
2024· article· fr· Formation et profession· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Entretien avec Philippe Artières
Alice Aterianus‐Owanga, Nora Greani, Philippe Artières
2016· article· fr· Gradhiva· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Sondage sur la capacité des services institutionnels de gestion de données de recherche Rapport INSIGHTS no3 Avenir du soutien à la GDR pour les établissements : ressources priorisées, investissements, défis et accélérateurs
Alexandra Cooper, Lucia Costanzo, Dylanne Dearborn, Carol Perry, Andrea Szwajcer, Minglu Wang
2021· report· fr· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · metaresearchconsensus · none
1
citations
venueno affunlabeled
Introduction
Marc-Éric Bobillier Chaumon
2007· article· fr· Perspectives interdisciplinaires sur le travail et la santé· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
1
citations
aboutno affunlabeled
Changement de perspective
Johanne Lamoureux
2007· book-chapter· fr· Presses de l’Université de Montréal eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
TÉNUS LIENS TENUS ENTRE LES ARTS
Béatrice Bloch
2017· article· fr· Revue de recherches en littératie médiatique multimodale· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Chapitre 10. La littératie technologique
Anne-Sophie Letellier
2016· book-chapter· fr· Presses de l’Université de Montréal eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
À la recherche du livre perdu
Stéphanie Fretz
2018· book-chapter· fr· Éditions ies eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About