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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
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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 10 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.

afffundunlabeled
Profiling misinformation susceptibility
Yara Kyrychenko, Hyunjin J. Koo, Rakoen Maertens, Jon Roozenbeek, Sander van der Linden, Friedrich M. Götz
2025· article· en· Personality and Individual Differences· Social Sciences
distilled prediction:candidate · noneconsensus · none
16
citations
fundno affunlabeled
Political Understanding
Maxime Lepoutre
2022· article· en· British Journal of Political Science· Social Sciences
distilled prediction:candidate · sts+insufficient_payloadconsensus · none
16
citations
afffundunlabeled
Early Detection of Rumor Veracity in Social Media
Anh Duc Dang, Abidalrahman Moh’d, Aminul Islam, Evangelos Milios
2019· article· en· Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences· Social Sciences
distilled prediction:candidate · metaepi_narrow+open_scienceconsensus · none
16
citations
aboutno affunlabeled
Dinner with Bayes: On the revision of risk beliefs
Christoph M. Rheinberger, James K. Hammitt
2018· article· en· Journal of Risk and Uncertainty· Social Sciences
distilled prediction:candidate · noneconsensus · none
16
citations
afffundunlabeled
Mixed News about the Bad News Game
Megan E. Graham, Brittany Skov, Zoë M. Gilson, Calvin Heise, Kaitlyn M. Fallow, Eric Y. Mah +1 more
2023· article· en· Journal of Cognition· Social Sciences
distilled prediction:candidate · noneconsensus · none
14
citations
affno abstractunlabeled
Mining for Fake News
Renz M. Cabusas, Brenna N. Epp, Justin M. Gouge, Tyson N. Kaufmann, Carson K. Leung, James R. A. Tully
2022· book-chapter· en· Lecture notes in networks and systems· Social Sciences
distilled prediction:candidate · noneconsensus · none
14
citations
aboutno affunlabeled
COVID-19: Belief in Conspiracy Theories and the Need for Quarantine
Марина Егорова, Оксана Паршикова, Yulia D. Chertkova, В. М. Староверов, Olga V. Mitina
2020· article· en· Psychology in Russia State of Art· Social Sciences
distilled prediction:candidate · noneconsensus · none
14
citations
affunlabeled
"Positive Energy"
Zhicong Lu, Yue Jiang, Chenxinran Shen, Margaret Jack, Daniel Wigdor, Mor Naaman
2021· article· en· Proceedings of the ACM on Human-Computer Interaction· Social Sciences
distilled prediction:candidate · noneconsensus · none
14
citations

How this was built: Screen · Findings · About