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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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Proceedings of the International Conference on Networked 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.

affaffiliation
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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.

73 results · 1 filter active ·
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20002024
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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.
73 works in the cohort · of 4,299,418page 2 of 2

Labels cover 0 of 73 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 73 of 73 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.

affunlabeled
Symposium 6: Participatory design in PALETTE project
Bernadette Charlier, Amaury Daele, Lilliane Esnault, France Henri, Murray Saunders
2008· article· en· Proceedings of the International Conference on Networked Learning· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Openness: Student perceptions
Michelle Harrison
2024· article· en· Proceedings of the International Conference on Networked Learning· Computer Science
machine prediction:candidate · open_scienceconsensus · none
0
citations
fundno affunlabeled
Symposium 4: Mobile work-learning
Terrie Lynn Thompson
2014· article· en· Proceedings of the International Conference on Networked Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
The Director's Story
Jane Costello
2012· article· en· Proceedings of the International Conference on Networked Learning· Arts and Humanities
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Symposium 6: Collaborative Learning
Kewal S. Dhariwal
2006· article· en· Proceedings of the International Conference on Networked Learning· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
The politics of the delete button
2012· article· en· Proceedings of the International Conference on Networked Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Designing for Networked Learning in The Third Space
Gale Parchoma, Kristine Dreaver‐Charles, Dorothea Nelson
2018· article· en· Proceedings of the International Conference on Networked Learning· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Tutor Support
Philip Watland
2004· article· en· Proceedings of the International Conference on Networked Learning· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Symposium 3: Phenomenology and networked learning - a found chord
Mike Johnson, Felicity Healey-Benson, Catherine Adams, Nina Bonderup Dohn, Greta Goetz
2024· article· en· Proceedings of the International Conference on Networked Learning· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Makerspaces as complex sociomaterial assemblages
Marguerite Koole, Kerry-Anne Anderson, Jay Wilson
2018· article· en· Proceedings of the International Conference on Networked Learning· Arts and Humanities
machine prediction:candidate · stsconsensus · none
0
citations
affunlabeled
Scaling engagement in MOOCs 4D
Matha Cleveland-Innes, Nathaniel Ostashewski, Dan Wilton
2024· article· en· Proceedings of the International Conference on Networked Learning· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
How do we know who we are when we’re online?
Bonnie Stewart
2014· article· en· Proceedings of the International Conference on Networked Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Assessment in clinical simulation
Andrew West, Gale Parchoma
2016· article· en· Proceedings of the International Conference on Networked Learning· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
The Supply Chain Collaboration Online Research Simulator
Kewal Dhariwal, Peter Carr
2004· article· en· Proceedings of the International Conference on Networked Learning· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About