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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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Digital Access to Scholarship at Harvard (DASH) (Harvard University)
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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.

152 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.
152 works in the cohort · of 4,299,418page 1 of 4

Labels cover 0 of 152 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 152 of 152 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
Multi-Task Bayesian Optimization
Kevin Swersky, Jasper Snoek, Ryan P. Adams
2013· article· en· Digital Access to Scholarship at Harvard (DASH) (Harvard University)· Computer Science
machine prediction:candidate · noneconsensus · none
452
citations
affunlabeled
Randomized Optimum Models for Structured Prediction
Daniel Tarlow, Ryan P. Adams, Richard S. Zemel
2012· article· en· Digital Access to Scholarship at Harvard (DASH) (Harvard University)· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affunlabeled
Electoral Rules and Minority Representation in U.S. Cities
Francesco Trebbi, Philippe Aghion, Alberto Alesina
2008· preprint· en· Digital Access to Scholarship at Harvard (DASH) (Harvard University)· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
31
citations
aboutno affunlabeled
Markets as Regulators: A Survey
Stavros Gadinis, Howell E. Jackson
2007· article· en· Digital Access to Scholarship at Harvard (DASH) (Harvard University)· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Probabilistic n-Choose-k Models for Classification and Ranking
Kevin Swersky, Brendan J. Frey, Daniel Tarlow, Richard S. Zemel, Ryan P. Adams
2012· article· en· Digital Access to Scholarship at Harvard (DASH) (Harvard University)· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
VLBI for Gravity Probe B. I. Overview
N. Bartel, M. F. Bietenholz, D. E. Lebach, J. F. Lestrade, R. R. Ransom, M. I. Ratner
2012· article· en· Digital Access to Scholarship at Harvard (DASH) (Harvard University)· Earth and Planetary Sciences
machine prediction:candidate · noneconsensus · none
7
citations
aboutno affunlabeled
Gains from Trade When Firms Matter
Marc J. Melitz, Daniel Trefler
2012· preprint· en· Digital Access to Scholarship at Harvard (DASH) (Harvard University)· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
3
citations
aboutno affunlabeled
States, Employers, and Gender Equality
Audrey Shannon Latura
2021· dissertation· en· Digital Access to Scholarship at Harvard (DASH) (Harvard University)· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Looking back at Social Knowledge in the Making
Michèle Lamont, Charles Camic, Neil Gross
2014· article· en· Digital Access to Scholarship at Harvard (DASH) (Harvard University)· Social Sciences
machine prediction:candidate · stsconsensus · none
1
citations

How this was built: Screen · Findings · About