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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.

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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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Credit Risk and Financial Regulations
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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.

847 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.
847 works in the cohort · of 4,299,418page 17 of 17

Labels cover 1 of 847 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 847 of 847 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.

affgemma · no categorygpt · no categorymodels agree
Reserves and Risk: Evidence from China
Rasmus Fatum, Takahiro Hattori, Yohei Yamamoto
2020· article· en· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Default Dependence and CDO Valuation
Peter Christoffersen, Jan Ericsson, Kris Jacobs, Xisong Jin
2009· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
An intensity‐based approach for equity modeling
Marcos Escobar, Tim Friederich, Mikhail Krayzler, Luis Seco, Rudi Zagst
2011· article· en· Applied Stochastic Models in Business and Industry· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
VIX maturity interpolation
Torben G. Andersen, Oleg Bondarenko, María T. González-Pérez
2025· article· en· Review of Derivatives Research· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
WHAT ARE INFLATION INDEXED
2011· article· en· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
HEC Montreal
2008· article· en· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Price Rigidities and Credit Risk
Patrick Augustin, Linxiao Cong, Alexandre Corhay, Michael Weber
2025· article· en· Journal of Financial and Quantitative Analysis· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
ESG und Kapitalkosten im M&A-Prozess
Ulf Füllgraf, Benjamin Badel
2024· book-chapter· de· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
afffundno abstractunlabeled
NORTA for portfolio credit risk
Mohamed Ayadi, Hatem Ben‐Ameur, Nabil Channouf, Quang-Khoi Tran
2018· article· en· Annals of Operations Research· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affaboutunlabeled
Do banks overstate their Value-at-Risk?
Christophe Pérignon, Zi Yin Deng, Zhi Jun Wang
2008· preprint· en· RePEc: Research Papers in Economics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
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