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 Differences and Values
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.

1,517 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.
1,517 works in the cohort · of 4,299,418page 23 of 31

Labels cover 4 of 1,517 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 1,517 of 1,517 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.

fundno affunlabeled
Emotions and Identity
2017· book· en· Psychology
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Beyond Whom and When
Ning Zhang, Li‐Jun Ji
2015· article· en· Journal of Cross-Cultural Psychology· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
Comparing self‐esteem discrepancies in Pakistan and Canada
Thomas I. Vaughan‐Johnston, Faizan Imtiaz, Li‐Jun Ji, Rubina Hanif, Devin I. Fowlie, Jill A. Jacobson
2023· article· en· Asian Journal Of Social Psychology· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
On Social Mentality: Chinese Anxiety
Li Zeng
2014· article· en· Cross-cultural communication· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Individualism-Collectivism as Cultural Chasm
Harry Nejad, Fara Nejad
2022· book-chapter· en· Advances in human resources management and organizational development book series· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Intercultural communication and training
John W. Berry, Ype H. Poortinga, Seger M. Breugelmans, Athanasios Chasiotis, David L. Sam
2011· book-chapter· en· Cambridge University Press eBooks· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Models for Paired Comparisons
Ulf Böckenholt
2005· book-chapter· en· Encyclopedia of Social Measurement· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Measuring Self-Sentiments
Neil J. MacKinnon
2015· book-chapter· en· Palgrave Macmillan UK eBooks· Psychology
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About