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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.

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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.

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Urban, Neighborhood, and Segregation Studies
Retraction
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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.

1,147 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,147 works in the cohort · of 4,299,418page 19 of 23

Labels cover 1 of 1,147 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 1,147 of 1,147 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
La Época de las Calamidades
2004· other· en· OhioLink ETD Center (Ohio Library and Information Network)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Community profile, Worklink Workforce Investment Area
2013· article· en· The South Carolina State Library Digital Collections (South Carolina State Library)· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Who gets left behind by left behind places?
Dylan S. Connor, A. Berg, Thomas Kemeny, Peter Kedron
2024· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Who gets left behind by left behind places?
Dylan S. Connor, A. Berg, Thomas Kemeny, Peter Kedron
2024· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
How should we study racial segregation?
Patricia Burke Wood
2019· article· en· Dialogues in Human Geography· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Little Housing, Big Rent On The Prairie
2025· article· en· DOAJ (DOAJ: Directory of Open Access Journals)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Kinder, Gentler Cuts
Mike Konczal
2012· article· en· Dissent· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
What Sparkles Does Not Always Shine
Simon Topp
2021· article· en· Contemporary Kanata Interdisciplinary Approaches To Canadian Studies· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
An Unbroken Red Line
August Bourré
2023· article· en· The iJournal Student Journal of the Faculty of Information· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Metropolitan View Of Toronto
2012· other· en· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
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How this was built: Screen · Findings · About