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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.

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Digital Communication and Language
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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.

490 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
490 works in the cohort · of 4,299,418page 1 of 10

Labels cover 2 of 490 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 490 of 490 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
How Readability Shapes Social Media Engagement
Ethan Pancer, Vincent Chandler, Maxwell Poole, Theodore J. Noseworthy
2018· article· en· Journal of Consumer Psychology· Computer Science
machine prediction:candidate · noneconsensus · none
124
citations
affno abstractunlabeled
lol: new language and spelling in instant messaging
Connie K. Varnhagen, G. Peggy McFall, Nicole E. Pugh, Lisa Routledge, Heather Sumida-MacDonald, Trudy E. Kwong
2009· article· en· Reading and Writing· Computer Science
machine prediction:candidate · noneconsensus · none
124
citations
aboutno affunlabeled
Discourse 2.0: Language and New Media
2013· book· en· Georgetown University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
87
citations
affunlabeled
Advertising Discourse
Marcel Danesi
2015· other· en· The International Encyclopedia of Language and Social Interaction· Computer Science
machine prediction:candidate · noneconsensus · none
71
citations
affunlabeled
The Structure of Language
Emma L. Pavey
2010· book· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
68
citations
affunlabeled
“Should Be Fun—Not!”
Juanita M. Whalen, Penny M. Pexman, Alastair J. Gill
2009· article· en· Journal of Language and Social Psychology· Computer Science
machine prediction:candidate · noneconsensus · none
61
citations
affaboutunlabeled
Voice and Persuasion in a Banking Telemarketing Context
Jean‐Charles Chebat, Kamel El Hedhli, Claire Gélinas‐Chebat, Robert Boivin
2007· article· en· Perceptual and Motor Skills· Computer Science
machine prediction:candidate · noneconsensus · none
52
citations
aboutno affunlabeled
SMS Communication: A Linguistic Approach
Louise‐Amélie Cougnon, Cédrick Fairon
2014· book· en· Computer Science
machine prediction:candidate · noneconsensus · none
50
citations
aboutno affunlabeled
Dimensions of Self-Expression in Facebook Status Updates
Adam Kramer, Cindy K. Chung
2021· article· en· Proceedings of the International AAAI Conference on Web and Social Media· Computer Science
machine prediction:candidate · noneconsensus · none
47
citations

How this was built: Screen · Findings · About