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
Teaching and Learning Programming
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.

980 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.
980 works in the cohort · of 4,299,418page 2 of 20

Labels cover 2 of 980 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 980 of 980 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
Designing Computer Science Competency Statements
Alison Clear, Tony Clear, Abhijat Vichare, Thea Charles, Stephen Frezza, Mirela Gutica +6 more
2020· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
45
citations
affunlabeled
Learning by doing
Karen Reid, Greg Wilson
2005· article· en· ACM SIGCSE Bulletin· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
venueno affno abstractunlabeled
All Cycles are Edge-Magic.
Omer Berkman, Michal Parnas, Y. Roditty
2001· article· en· Ars Combinatoria· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affunlabeled
How much choice is too much?
Katrin Becker
2006· article· en· ACM SIGCSE Bulletin· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affunlabeled
Facilitating code-writing in PI classes
Daniel Zingaro, Yuliya Cherenkova, Olessia Karpova, Andrew Petersen
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
43
citations
affunlabeled
Gr8 designs for Gr8 girls
Michelle Craig, Diane Horton
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
43
citations
affunlabeled
CS circles
David Pritchard, Troy Vasiga
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
42
citations
affunlabeled
Novice Programmers and the Problem Description Effect
Dennis Bouvier, Ellie Lovellette, John Matta, Bedour Alshaigy, Brett A. Becker, Michelle Craig +4 more
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
41
citations
affunlabeled
More than the code
Josh Tenenberg, Wolff‐Michael Roth, Donald Chinn, Alfredo Jornet, David Socha, Skip Walter
2018· article· en· Communications of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affunlabeled
Nifty Assignments
Nick Parlante, Julie Zelenski, Ben Stephenson, Ali Malik, Phil Ventura, Michael Guerzhoy +2 more
2018· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
40
citations
fundno affunlabeled
Information technology and indigenous communities
Lyndon Ormond-Parker, Aaron Corn, Cressida Fforde, Kazuko Obata, Sandy O’Sullivan
2013· book· en· ANU Open Research (Australian National University)· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
affunlabeled
Misconceptions about computer science
Peter J. Denning, Matti Tedre, Pat Yongpradit
2017· article· en· Communications of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
29
citations
affunlabeled
Learning by doing
Karen Reid, Greg Wilson
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations

How this was built: Screen · Findings · About