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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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Decision-Making and Behavioral Economics
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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.

1,177 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.
1,177 works in the cohort · of 4,299,418page 2 of 24

Labels cover 1 of 1,177 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 1,177 of 1,177 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.

affunlabeled
Evolution and Human Nature
Arthur J. Robson
2002· article· en· The Journal of Economic Perspectives· Decision Sciences
machine prediction:candidate · noneconsensus · none
164
citations
affunlabeled
Framing Effects in Younger and Older Adults
S. Kim, Daniel G. Goldstein, Lynn Hasher, Rose T. Zacks
2005· article· en· The Journals of Gerontology Series B· Decision Sciences
machine prediction:candidate · noneconsensus · none
161
citations
afffundunlabeled
Base rates: Both neglected and intuitive.
Gordon Pennycook, Dries Trippas, Simon J. Handley, Valerie A. Thompson
2013· article· en· Journal of Experimental Psychology Learning Memory and Cognition· Decision Sciences
machine prediction:candidate · noneconsensus · none
160
citations
fundno affno abstractunlabeled
The risk perceptions of individual investors
Chris Veld, Yulia V. Veld‐Merkoulova
2007· article· en· Journal of Economic Psychology· Decision Sciences
machine prediction:candidate · noneconsensus · none
133
citations
afffundno abstractunlabeled
Confidence and accuracy in deductive reasoning
Jody M. Shynkaruk, Valerie A. Thompson
2006· article· en· Memory & Cognition· Decision Sciences
machine prediction:candidate · noneconsensus · none
131
citations
affno abstractunlabeled
A Practitioner's Guide to Nudging
Kim Ly, Nina Mažar, Min Zhao, Dilip Soman
2013· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
127
citations
afffundunlabeled
Extreme Outcomes Sway Risky Decisions from Experience
Elliot A. Ludvig, Christopher R. Madan, Marcia L. Spetch
2013· article· en· Journal of Behavioral Decision Making· Decision Sciences
machine prediction:candidate · noneconsensus · none
117
citations
affunlabeled
Disfluent fonts don’t help people solve math problems.
Andrew Meyer, Shane Frederick, Terence C. Burnham, Juan D. Pinto, Ty W. Boyer, Linden J. Ball +4 more
2015· review· en· Journal of Experimental Psychology General· Decision Sciences
machine prediction:candidate · noneconsensus · none
113
citations
fundno affunlabeled
Do smart people have better intuitions?
Valerie A. Thompson, Gordon Pennycook, Dries Trippas, Jonathan St. B. T. Evans
2018· article· en· Journal of Experimental Psychology General· Decision Sciences
machine prediction:candidate · noneconsensus · none
110
citations

How this was built: Screen · Findings · About