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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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Electoral Systems and Political Participation
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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.

1,890 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.
1,890 works in the cohort · of 4,299,418page 20 of 38

Labels cover 4 of 1,890 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 1,890 of 1,890 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
Battleground
Daron R. Shaw, Scott L. Althaus, Costas Panagopoulos
2024· book· en· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
4
citations
affunlabeled
Quality Control
Austin Hart, J. Scott Matthews
2023· book· en· Cambridge University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Do voters prefer more parties on the ballot?
John Högström, André Blais, Carolina Plescia
2021· article· en· Acta Politica· Social Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affaboutunlabeled
2019 Canadian Election Study (CES) - Phone Survey
Laura B. Stephenson, Allison Harell, Daniel Rubenson, Peter John Loewen
2020· dataset· en· Harvard Dataverse· Social Sciences
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
Risk aversion and strategic voting
Danielle Martin
2021· article· en· International Journal of Public Opinion Research· Social Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affaboutunlabeled
Opposition parties in times of pandemics
Lydia Laflamme, Jeanne Milot-Poulin, Jeanne Desrosiers, Cedrik Verreault, Carolane Fillion, Nicolas Patenaude +1 more
2023· article· en· Journal of Legislative Studies· Social Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
The US Congress and Rogue States
Shereen Kotb, Gyung‐Ho Jeong
2021· article· en· Foreign Policy Analysis· Social Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affaboutunlabeled
Representation at the margins
Marc André Bodet
2011· article· en· Party Politics· Social Sciences
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
Votes at 16? How the Rest of the World Does it
Christine Huebner, Constanza Sanhueza Petrarca
2024· article· en· Political Insight· Social Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Words as Data
Shane Martin, Thomas Saalfeld, Kaare W. Strøm, Jonathan Slapin, Sven‐Oliver Proksch
2014· book-chapter· en· Oxford University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About