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 23 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.

affunlabeled
A comparison of Range Value at Risk (RVaR) forecasting models
Fernanda Maria Müller, Thalles Weber Gössling, Samuel Solgon Santos, Marcelo Brutti Righi
2023· article· en· Journal of Forecasting· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
fundno affunlabeled
Extremal connectedness of hedge funds
Linda Mhalla, Julien Hambuckers, Marie Lambert
2022· article· en· Journal of Applied Econometrics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
venueno affno abstractunlabeled
The Brent-WTI spread revisited: A novel approach
Isabella Ruble, John Powell
2021· article· en· The Journal of Economic Asymmetries· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
The Economics of Oil, Biofuel and Food Commodities
Eric Bahel, Walid Marrouch, Gérard Gaudet
2011· preprint· en· RePEc: Research Papers in Economics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Commodities and Policy Uncertainty Channel(s)
K. Smimou, David D. Bosch, Greg Filbeck
2024· article· en· International Review of Economics & Finance· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
What Drives Commodity Price Booms and Busts?
David S. Jacks, Martin Stuermer
2016· article· en· Federal Reserve Bank of Dallas, Working Papers· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
Flip the Coin: Heads, Tails or Cryptocurrencies?
António Portugal Duarte, Fátima Sol Murta, Nuno Baetas da Silva, Beatriz Rodrigues Vieira
2023· article· en· Scientific Annals of Economics and Business· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About