MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
→
Sort
Language
Type
Field
Venue
Topic
Market Dynamics and Volatility
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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.

2,530 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
2,530 works in the cohort · of 4,299,418page 35 of 51

Labels cover 4 of 2,530 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 2,530 of 2,530 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.

affno abstractunlabeled
The Allen-Unger Global Commodity Prices Database
Robert F. Allen, Richard W. Unger
2019· article· en· Journal of humanities and social sciences· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Fuel Sales Forecasting with SARIMA-GARCH and Rolling Window
Ramneet Singh Chadha, Jugesh, Shahzadi Parveen, Jasmehar Singh
2023· article· en· Journal of Soft Computing Paradigm· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Industry Effects of Oil Price Shocks: Re-Examination
Soojin Jo, Lilia Karnizova, Abeer Reza
2017· article· en· Federal Reserve Bank of Dallas, Working Papers· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
International Monetary Policy Spillovers
Dennis Nsafoah, Apostolos Serletis
2018· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affno abstractunlabeled
The response of state employment to oil price volatility
Wei Kang, David A. Penn, Joachim Zietz
2013· article· en· Journal of Economics and Finance· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
affvenueaboutunlabeled
Oil Price Shocks and the Canadian Stock Market
Wei Dai
2024· article· en· Journal of risk and financial management· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Crude oil prices and its role in economy
Kapil Jain, Shine David
2013· article· en· International Journal of Managment, IT and Engineering· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
The Brexit and investors' fear
Imlak Shaikh
2018· article· en· Ekonomski pregled· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About