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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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Media Influence and Politics
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.

319 results · 1 filter active ·
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20012025
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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.
319 works in the cohort · of 4,299,418page 2 of 7

Labels cover 1 of 319 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 319 of 319 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
The Runner-Up Effect
Santosh Anagol, Thomas Fujiwara
2014· report· en· National Bureau of Economic Research· Social Sciences
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
A Computational Framework for Media Bias Mitigation
Souneil Park, Seungwoo Kang, Sangyoung Chung, Junehwa Song
2012· article· en· ACM Transactions on Interactive Intelligent Systems· Social Sciences
machine prediction:candidate · noneconsensus · none
20
citations
affunlabeled
Preview Provision Under Competition
Xiang Yi, David Soberman
2010· article· en· Marketing Science· Social Sciences
machine prediction:candidate · noneconsensus · none
18
citations
affno abstractunlabeled
Media Versus Special Interests
I. J. Alexander Dyck, David A. Moss, Luigi Zingales
2008· article· en· SSRN Electronic Journal· Social Sciences
machine prediction:candidate · noneconsensus · none
18
citations
aboutno affunlabeled
Outsourced Credibility?
J. David Martin, Ralph J. Martins
2016· article· en· Journalism Studies· Social Sciences
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Monetary Policy in the Media
Helge Berger, Michael Ehrmann, Marcel Fratzscher
2006· article· en· Journal of money credit and banking· Social Sciences
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
The Economics of Social Media
Guy Aridor, Rafael Jiménez Durán, Roee Levy, Lena Song
2024· article· en· SSRN Electronic Journal· Social Sciences
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
The Politics of Personalized News Aggregation
Lin Hu, Anqi Li, Ilya Segal
2023· article· en· Journal of Political Economy Microeconomics· Social Sciences
machine prediction:candidate · noneconsensus · none
12
citations
fundno affno abstractunlabeled
Creating confusion
Chris Edmond, Yang Lu
2020· article· en· Journal of Economic Theory· Social Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Weak Versus Strong Net Neutrality
Joshua S. Gans
2014· preprint· en· National Bureau of Economic Research· Social Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Overconfidence in Political Behavior
Pietro Ortoleva, Erik Snowberg
2013· report· en· National Bureau of Economic Research· Social Sciences
machine prediction:candidate · noneconsensus · none
9
citations

How this was built: Screen · Findings · About