MétaCan
ENFR
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.

Export CSVExport JSONAPI query

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
Technology Adoption and User Behaviour
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.

1,648 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.
1,648 works in the cohort · of 4,299,418page 28 of 33

Labels cover 4 of 1,648 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 1,648 of 1,648 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.

fundno affunlabeled
Quo vadis TAM?
2007· article· en· Journal of the Association for Information Systems· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Education Technology: An Evidence-Based Review
Maya Escueta, Vincent Quan, Andre Nickow, Philip Oreopoulos
2017· article· en· National Bureau of Economic Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
A process for co-creating shared value with the crowd
Vincent Grèzes, Béatrice Girod Lehmann, Marc Schnyder, Antoine Perruchoud
2016· article· en· Technology Innovation Management Review· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
[Not Available].
Lise Rénaud, Monique Caron Bouchard
2010· article· en· PubMed· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affunlabeled
Usersâ Loyalty towards Mobile Banking in Malaysia
Yun Min Low, Goh Chin Fei, Tan Owee Kowang, Amran Rasli
2017· article· en· The Journal of Internet Banking and Commerce· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Mobile Device Access: Effect on Online Purchases
Ruiqi Yan, Brian Paul Cozzarin, Stanko Dimitrov
2015· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Using overview style tables on small devices
Rui Zhang
2006· article· en· Journal of the Association for Information Systems· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
A Benchmark for B2B Use of E-Commerce
Scott Fosdick, Bryan H. Reber
2005· article· en· Journal of Internet Commerce· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
← previousnext →

How this was built: Screen · Findings · About