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
Topic
Disaster Management and Resilience
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.

1,725 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.
1,725 works in the cohort · of 4,299,418page 28 of 35

Labels cover 2 of 1,725 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 1,725 of 1,725 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.

venueno affno abstractunlabeled
10.1016/s0029-7437(06)72212-1
2000· article· en· Time to knit· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Natural Disaster In Canada (2024)
2025· preprint· en· ArXiv.org· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
fundno affno abstractunlabeled
Population and housing recovery in Tōhoku, Japan
David W. Edgington
2023· article· en· International Journal of Disaster Risk Reduction· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Introduction to the special issue
Ahmed Shafiqul Huque, Habib Zafarullah
2023· article· en· Public Administration and Policy· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Before the Flood
Geoffrey S. Baird, Trevor Bedford, Michael Boeckh, Chloe Bryson‐Cahn, Helen Y. Chu, Seth A. Cohen +39 more
2020· article· en· Clinical Infectious Diseases· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Boots on the Ground
Johanu Botha
2022· book· en· University of Toronto Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
The Politics of Natural Disasters
Daniel P. Aldrich
2013· article· en· Oxford Bibliographies Online Datasets· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
3.11: Disaster and Change in Japan.
Daniel P. Aldrich
2014· article· en· Pacific Affairs· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
← previousnext →

How this was built: Screen · Findings · About