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
Advanced studies in theoretical and applied econometrics
Topic
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.

11 results · 1 filter active ·
Results by year
20042025
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.
11 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 11 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 11 of 11 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 “Probabilistic Revolution”
Peter Tryfos
2004· book-chapter· en· Advanced studies in theoretical and applied econometrics· Mathematics
machine prediction:candidate · noneconsensus · none
28
citations
affno abstractunlabeled
Nonparametric Models with Random Effects
Yiguo Sun, Wei Lin, Qi Li
2024· book-chapter· en· Advanced studies in theoretical and applied econometrics· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Demand for Transport and Communication
José María Mella Márquez, F.G. Adams, P. Balestra, M.G. Dagenais, Denise Kendrick, J.H.P. Paelinck +2 more
2005· book-chapter· en· Advanced studies in theoretical and applied econometrics· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Food Demand Analysis in Australia
Jaime Márquez, Faizal Adams, P. Balestra, M.G. Dagenais, D. Kendrick, Jean H. P. Paelinck +2 more
2005· book-chapter· en· Advanced studies in theoretical and applied econometrics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Problems in Non-Linear Engel Functions
Juan Bosco Romero Márquez, F. Gérard Adams, Pietro Balestra, M.G. Dagenais, David A. Kendrick, Jean H. P. Paelinck +2 more
2005· book-chapter· en· Advanced studies in theoretical and applied econometrics· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Nonparametric Models with Random Effects
Yiguo Sun, Wei Lin, Qi Li
2017· book-chapter· en· Advanced studies in theoretical and applied econometrics· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Dynamic Log-Linear Probability Model with Interactions
Christian Gouriéroux, Nour Meddahi
2025· book-chapter· en· Advanced studies in theoretical and applied econometrics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Estimating Increase in Consumer Demand
2005· book-chapter· en· Advanced studies in theoretical and applied econometrics· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About