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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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Emotional Labor in Professions
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.

372 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
372 works in the cohort · of 4,299,418page 2 of 8

Labels cover 1 of 372 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 372 of 372 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.

afffundaboutunlabeled
Role Identity, Dissonance, and Distress among Paramedics
Justin Mausz, Elizabeth Donnelly, Sandra Moll, Sheila Harms, Meghan McConnell
2022· article· en· International Journal of Environmental Research and Public Health· Social Sciences
machine prediction:candidate · noneconsensus · none
36
citations
affno abstractunlabeled
Service, emotional labor, and mindfulness
Emma Wang, Pierre Berthon, Leyland Pitt, Ian P. McCarthy
2016· article· en· Business Horizons· Social Sciences
machine prediction:candidate · noneconsensus · none
32
citations
affaboutunlabeled
‘We deal with human beings’
Laura Huey, Hina Kalyal
2017· article· en· International Journal of Police Science & Management· Social Sciences
machine prediction:candidate · noneconsensus · none
20
citations
affunlabeled
Learning racial hierarchies
Kiran Mirchandani
2012· article· en· Journal of Workplace Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Helping Without Caring
Francesca Polletta, Zaibu Tufail
2016· article· en· Work and Occupations· Social Sciences
machine prediction:candidate · noneconsensus · none
17
citations
affvenueaboutunlabeled
The Emotional Labour of Public Library Work
Joanne Rodger, Norene Erickson
2021· article· en· Partnership The Canadian Journal of Library and Information Practice and Research· Social Sciences
machine prediction:candidate · noneconsensus · none
17
citations
afffundunlabeled
Language put to Work
Enda Brophy
2011· article· en· Journal of Communication Inquiry· Social Sciences
machine prediction:candidate · noneconsensus · none
16
citations
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How this was built: Screen · Findings · About