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
Media Studies and Communication
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.

1,390 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.
1,390 works in the cohort · of 4,299,418page 28 of 28

Labels cover 4 of 1,390 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 1,390 of 1,390 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.

afffundunlabeled
Journalistic Role Performance in Times of COVID
Daniel C. Hallin, Claudia Mellado, Akiba A. Cohen, Nicolas Hubé, David Nolan, Gabriella Szabó +28 more
2023· article· en· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Consuming Celebrity
Marlis Schweitzer
2017· book-chapter· en· Punctum Books· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueno abstractunlabeled
Lies the Media Tell Us
Jeffery Klaehn
2009· article· en· Canadian Journal of Communication· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Review of the 2011 IAMCR Conference
Scott Timcke, Graeme Webb, Jay McKinnon
2012· article· en· Stream Interdisciplinary Journal of Communication· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Representing the Underrepresented
Darnell Wyke
2024· article· en· The Motley Undergraduate Journal· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Studying Global News: Methodological Issues
2016· article· en· San José State University ScholarWorks (San Jose State University)· Social Sciences
machine prediction:candidate · metaresearchconsensus · none
0
citations
aboutno affunlabeled
Yuruganu miyo kaname-no-ishizue
2015· other· en· Open Collections· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affvenueno abstractunlabeled
Media Theory: Chasing Ambulances?
Michael Dorland
2017· article· en· Canadian Journal of Communication· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
My Media Studies
Rebecca Sullivan
2008· article· en· Television & New Media· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About