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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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Impact of Technology on Adolescents
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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
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venuejournal
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

2,105 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.
2,105 works in the cohort · of 4,299,418page 2 of 43

Labels cover 6 of 2,105 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 2,105 of 2,105 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
Checking email less frequently reduces stress
Kostadin Kushlev, Elizabeth W. Dunn
2014· article· en· Computers in Human Behavior· Social Sciences
machine prediction:candidate · noneconsensus · none
225
citations
affunlabeled
Personal Relationships: On and Off the Internet
Jeffrey Boase, Barry Wellman
2006· book-chapter· en· Cambridge University Press eBooks· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
207
citations
venueno affunlabeled
Facebook groups as LMS: A case study
Hagit Meishar‐Tal, Gila Kurtz, Efrat Pieterse
2012· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
192
citations
affaboutunlabeled
Facebook
Aaron M. Fewkes, Mike McCabe
2012· article· en· Journal of Digital Learning in Teacher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
181
citations
affno abstractunlabeled
Behavioral Addictions
Jeffrey L. Derevensky, Victoria Hayman, Lynette Gilbeau
2019· review· en· Pediatric Clinics of North America· Social Sciences
machine prediction:candidate · noneconsensus · none
171
citations
venueno affunlabeled
Why Do Academics Use Academic Social Networking Sites?
Hagit Meishar‐Tal, Efrat Pieterse
2017· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · metaresearch+scholarly_communicationconsensus · none
148
citations
affunlabeled
Lost in Cyberspace: The Web @ Work
David Greenfield, Richard A. Davis
2002· article· en· CyberPsychology & Behavior· Social Sciences
machine prediction:candidate · noneconsensus · none
143
citations
affno abstractunlabeled
Friendship: An old concept with a new meaning?
Yair Amichai‐Hamburger, Mila Kingsbury, Barry H. Schneider
2012· article· en· Computers in Human Behavior· Social Sciences
machine prediction:candidate · noneconsensus · none
141
citations
affunlabeled
The screens culture: impact on ADHD
Margaret D. Weiss, S. Baer, Blake A. Allan, Kelly Saran, Heidi Schibuk
2011· review· en· ADHD Attention Deficit and Hyperactivity Disorders· Social Sciences
machine prediction:candidate · noneconsensus · none
138
citations

How this was built: Screen · Findings · About