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
Computer Supported Collaborative Learning
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.

53 results · 1 filter active ·
Results by year
20052019
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.
53 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 53 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 53 of 53 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
DALITE: Bringing "Peer-Instruction" Online.
Elizabeth S. Charles, Chris Whittaker, Michael Dugdale, Nathaniel Lasry, Sameer Bhatnagar, Kevin Lenton
2013· article· en· Computer Supported Collaborative Learning· Psychology
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
affno abstractunlabeled
The Use of Visual Evidence for Planning and Argumentation.
Rebecca Cober, Alisa Acosta, Michelle Lui, Tom Moher, Alex Kuhn, Chris Quintana +1 more
2015· article· en· Computer Supported Collaborative Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Designing for Distributed Regulatory Processes in CSCL.
Elizabeth S. Charles, Mariel Miller, Roger Azevedo, Allyson F. Hadwin, Susanne P. Lajoie
2013· article· en· Computer Supported Collaborative Learning· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Design in the World AND Our Work.
Richard Reeve, Vanessa Svihla
2013· article· en· Computer Supported Collaborative Learning· Arts and Humanities
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
The Digital Use Divide and Knowledge Building
Thérèse Laferrière, Alain Breuleux
2017· article· en· Computer Supported Collaborative Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About