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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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Society for Information Technology & Teacher Education International Conference
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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.

426 results · 1 filter active ·
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20002021
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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.
426 works in the cohort · of 4,299,418page 4 of 9

Labels cover 0 of 426 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 426 of 426 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
The Lead IT project: Leading and learning for student success
Marion A. Barfurth, Hélène Coulombe, Pierre Michaud
2006· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
A Case Study: Online Learning Pattern Analysis
Jiye Ai, Jikun Ai
2004· article· en· Society for Information Technology & Teacher Education International Conference· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
GrassRoots: The Diffusion of an Innovation
David Dibbon
2003· article· en· Society for Information Technology & Teacher Education International Conference· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Learning from the Local: Teachers’ Technology Mis/Practices
Chloë Brushwood Rose, Jennifer Jenson, Brian Lewis
2003· article· en· Society for Information Technology & Teacher Education International Conference· Arts and Humanities
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
The History of Edutainment, and Why It Matters
Katrin Becker
2012· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Beyond Times Roman
Gillian Mothersill
2002· article· en· Society for Information Technology & Teacher Education International Conference· Arts and Humanities
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About