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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.

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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Misinformation and Its Impacts
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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.

1,959 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.
1,959 works in the cohort · of 4,299,418page 11 of 40

Labels cover 16 of 1,959 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 1,959 of 1,959 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

venueno affunlabeled
Information Ethics: a student’s perspective
Sarah Kaddu
2007· article· en· The International Review of Information Ethics· Social Sciences
distilled prediction:candidate · metaresearchconsensus · none
13
citations
affunlabeled
The Psychology of Fake News
Gordon Pennycook, David G. Rand
2020· preprint· en· Social Sciences
distilled prediction:candidate · noneconsensus · none
13
citations
venueno affunlabeled
Fake Review Detection Using Machine Learning
Wesam Hameed Asaad, Ragheed Allami, Yossra Hussain Ali
2023· article· fr· Revue d intelligence artificielle· Social Sciences
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
13
citations
afffundunlabeled
What Makes News Sharable on Social Media?
X. Chen, Gordon Pennycook, David G. Rand
2021· preprint· en· Social Sciences
distilled prediction:candidate · scholarly_communication+insufficient_payloadconsensus · none
12
citations
afffundunlabeled
Nudging social media sharing towards accuracy
Gordon Pennycook, David G. Rand
2021· preprint· en· Social Sciences
distilled prediction:candidate · scholarly_communication+insufficient_payloadconsensus · none
12
citations
aboutno affunlabeled
All the world's a stage: Making sense of Shakespeare
Michael Olsson
2010· article· en· Proceedings of the American Society for Information Science and Technology· Social Sciences
distilled prediction:candidate · stsconsensus · none
12
citations
afffundno abstractunlabeled
Mitigating Misinformation and Changing the Social Narrative
Elissa M. Abrams, Matthew Greenhawt
2020· editorial· en· The Journal of Allergy and Clinical Immunology In Practice· Social Sciences
distilled prediction:candidate · metaresearch+research_integrityconsensus · none
11
citations
afffundunlabeled
A Twitter dataset for Monkeypox, May 2022
Zahra Movahedi Nia, Nicola Luigi Bragazzi, Jianhong Wu, Jude Dzevela Kong
2023· article· en· Data in Brief· Social Sciences
distilled prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Overview of the TREC 2020 Health Misinformation Track.
Charles L. A. Clarke, S. Nasir Aziz Rizvi, Mark D. Smucker, Maria Maistro, Guido Zuccon
2020· article· en· Text REtrieval Conference· Social Sciences
distilled prediction:candidate · insufficient_payloadconsensus · none
11
citations

How this was built: Screen · Findings · About