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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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The International Review of Research in Open and Distributed Learning
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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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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.

1,490 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,490 works in the cohort · of 4,299,418page 27 of 30

Labels cover 4 of 1,490 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 1,490 of 1,490 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.

affvenueunlabeled
Audio/ Videoconferencing Packages: Low cost
Remi Treblay, Barb Fyvie, Brenda Koritko
2006· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affvenueunlabeled
Emotion, Learning and the Online Learning Environment
Martha Cleveland‐Innes, Zehra Akyol
2008· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Book Review – Networked Learning
Mike Johnson
2016· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Net Neutrality and its Implications to Online Learning
Lisa C. Yamagata-Lynch, Deepa R Despande, Jaewoo Do, Erin Garty, Jason Mastrogiovanni, Stephanie J Teague
2017· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
"Chaos rules" revisited
David Murphy
2011· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affvenueunlabeled
Audio/ Videoconferencing Packages: High cost
Sonia Murillo, Mary Rizzuto, Urel Sawyers
2006· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affvenueunlabeled
Text-based Conferencing Products
Debbie Garber, Jennifer Stein, Jon Baggaley
2002· article· en· The International Review of Research in Open and Distributed Learning· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Advancing Online Learning in Asia
Insung Jung
2004· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affvenueaboutunlabeled
Blended online learning design: Shaken not stirred
Norm Vaughn, Michael Power
2010· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Enriching the Web of Data with Educational Information Using We-Share
Adolfo Ruiz‐Calleja, Juan I. Asensio‐Pérez, Guillermo Vega‐Gorgojo, Eduardo Gómez‐Sánchez, Miguel L. Bote‐Lorenzo, Carlos Alario‐Hoyos
2017· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Editorial – Volume 16, Issue Number 6
Markus Deimann, Sebastian Vogt
2015· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
venueno affno abstractunlabeled
Authors' Response to Eastmond's Commentary
Alfred P. Rovai, Hope Jordan
2004· article· en· The International Review of Research in Open and Distributed Learning· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affvenueunlabeled
Selection of Collaborative Tools
Tom Kane, Jon Baggaley
2002· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affvenueunlabeled
Integrated Collaborative Tools
Lynn Fujino, Neil Martindale, Sharon Mulder, Clare Woodward, Patrick J. Fahy
2002· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Editorial — Volume 26, Issue 2
Adnan Qayyum
2025· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

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