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
Canadian Policy and Governance
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.

9,787 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.
9,787 works in the cohort · of 4,299,418page 60 of 196

Labels cover 3 of 9,787 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 9,787 of 9,787 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 affno abstractunlabeled
CANADA-UNITED STATES RELATIONS
2015· book-chapter· en· University of Toronto Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Canada
2019· book-chapter· en· OECD economic outlook· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Chapter 1. Canada in the World
Tom Brzustowski
2008· book-chapter· fr· OpenEdition (OpenEdition)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Business debt outstanding in Canada
2024· other· en· Financing SMEs and entrepreneurs· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Notes from the Editors, June 2001
The Editors
2001· article· en· Monthly Review· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Conclusion
Pierre-Alexandre Beylier
2016· book-chapter· fr· Presses universitaires de Rennes eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Occurrence Download
2025· dataset· en· Global Biodiversity Information Facility· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
13. Canadian Ownership and Competition Policy
Robert W. Armstrong
2016· book-chapter· en· University of Toronto Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
8. Getting It Right: Australia and Canada
2015· book-chapter· en· Harvard University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Canada and Australia
Deganit Paikowsky
2017· book-chapter· en· Cambridge University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Introduction
Paul R. Masson
2019· book-chapter· en· WORLD SCIENTIFIC eBooks· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Build Toronto and Invest Toronto
2010· book-chapter· en· Local economic and employment development· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
MBS Deferred Asset Model
2022· article· en· Zenodo (CERN European Organization for Nuclear Research)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Happy Tariff Day!
2025· other· en· Internet Archive (Internet Archive)· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Ambulance fees rile Nova Scotians
Donalee Moulton
2002· article· en· Europe PMC (PubMed Central)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About