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
Online and Blended Learning
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.

3,545 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.
3,545 works in the cohort · of 4,299,418page 49 of 71

Labels cover 14 of 3,545 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 3,545 of 3,545 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
Using Tools to Enhance Achievement in Online Study
Rob McTavish
2011· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Novices satisfaction with e-learning of EXCEL
Samir Larhib, Michel Plaisent, Prosper Bernard, Lassana Maguiraga
2004· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
What I've Learned in University 101
Carlie McKenzie
2012· article· en· Divergent/Convergent· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
E-Learning as Nation Building
Marco Adria, Katy Campbell
2007· book-chapter· en· IGI Global eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
EvaICT: A Knowledge Base to Assess ICT Learning Environments
Aude Dufresne, Jacques Raynauld, Emmanuelle Villiot-Leclerc
2005· article· en· EdMedia: World Conference on Educational Media and Technology· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Nugget Notes: A Simple Teaching Tool
Kirk Plangger, Michael Parent
2015· book-chapter· en· Developments in marketing science: proceedings of the Academy of Marketing Science· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
fundno affunlabeled
Blended Learning Makes Customizable Learning a Reality
Jean Adams, Rita Hanesiak, Gareth Morgan, Ron Owston, Denys Lupshenyuk, Laura Mills
2010· article· en· York University Digital Library (York University)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Technology Drives Learning - Learning Never Ends
Fern Snart, Mike Carbonaro, Cheryel Goodale
2002· article· en· EdMedia: World Conference on Educational Media and Technology· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
A Classroom of One is a Community of Learners
Katherine J. Janzen, Beth Perry, Margaret Edwards
2022· article· en· Journal of Invitational Theory and Practice· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
ICT Ecologies of Learning:
Jenny Arntzen, Don Krug
2011· book-chapter· en· IGI Global eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
City University of New York: Digital Classroom Project
Christina W. Charnitski, Carol E. Dowling
2007· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Upload, Download, Overload!
Lena Paulo Kushnir, Kenneth Berry
2012· book-chapter· en· IGI Global eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About