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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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scientometrics and bibliometrics research
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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.

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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.

2,100 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.
2,100 works in the cohort · of 4,299,418page 15 of 42

Labels cover 193 of 2,100 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 2,100 of 2,100 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.

aboutno affunlabeled
Awareness of Altmetrics among LIS Scholars and Faculty
Sarah Sutton, Rachel Miles, Stacy Konkiel
2018· article· en· Journal of Education for Library and Information Science· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
9
citations
affvenuegemma · metaresearch+scholarly_communicationgpt · metaresearch+scholarly_communication+research_integritymodels split
Predatory Journals : Do Not Enter
Faizan Khan, David Moher
2017· article· en· University of Ottawa Journal of Medicine· Decision Sciences
machine prediction:candidate · metaresearch+research_integrityconsensus · none
9
citations
afffundunlabeled
On the topicality and research impact of special issues
Maxime Sainte-Marie, Philippe Mongeon, Vincent Larivière
2019· article· en· Quantitative Science Studies· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
9
citations
venueno affunlabeled
Incentives for Journal Editors
Jinyoung Kim, Kanghyock Koh
2014· article· en· Canadian Journal of Economics/Revue canadienne d économique· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
9
citations
affunlabeled
Uncited papers are not useless
M. Golosovsky, Vincent Larivière
2021· article· en· Quantitative Science Studies· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
9
citations
afffundvenueaboutgemma · bibliometrics+open_sciencegpt · bibliometrics+open_science+scholarly_communicationmodels split
Measuring the prevalence of open access in Canada: A national comparison
Virginie Paquet, Simon van Bellen, Vincent Larivière
2022· article· en· Canadian Journal of Information and Library Science· Decision Sciences
machine prediction:candidate · metaresearch+bibliometrics+open_scienceconsensus · none
8
citations
aboutno affunlabeled
Peer Review – the future is here
Maria Papatriantafyllou
2017· article· en· FEBS Letters· Decision Sciences
machine prediction:candidate · metaresearch+insufficient_payloadconsensus · none
8
citations
affvenueaboutunlabeled
Evaluating Academic Research Networks
Damien Contandriopoulos, Catherine Larouche, Arnaud Duhoux
2018· article· en· Canadian Journal of Program Evaluation· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
8
citations

How this was built: Screen · Findings · About