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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 2 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
Did they Forget? Understanding Teacher TPACK During the COVID-19 Pandemic
Daniel Mourlam, Steven R. Chesnut, Gabrielle A. Strouse, Daniel A. DeCino, Ryan Los, Lisa A. Newland
2021· article· en· Society for Information Technology & Teacher Education International Conference· Arts and Humanities
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Scaling the Digital Leadership Divide
Rick Mrazek, Maurice Hollingsworth, Marlo Steed
2005· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Twitter: Intellectual Stimulator or Attention Distracter
Lorraine Beaudin, Jennifer Deyenberg
2011· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affaboutno abstractunlabeled
Building Community through Telecollaboration (BCT) project in Quebec
Alain Breuleux, Gyeong Mi Heo, Ted Wall, Latoya Morgan, Luis Flores
2009· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Formative Computer-Based Assessment in Higher Education
Tess Miller, Lyn M. Shulha
2008· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Geotrekking: Connecting Education to the Real World
Tim Pelton, Leslee Francis Pelton, Karen Moore
2007· article· en· Society for Information Technology & Teacher Education International Conference· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Three Reasons to Use Twitter in Teacher Preparation
Lorraine Beaudin, Tessa Sivak
2015· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Using Digital Imagery in Learning: A New Literacy
Kelly Edmonds
2006· article· en· Society for Information Technology & Teacher Education International Conference· Health Professions
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Helping students learn with classroom response systems
Tim Pelton, Leslee Francis Pelton
2005· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Virtual Reality Worlds for Teacher Education
Marlo Steed
2014· article· en· Society for Information Technology & Teacher Education International Conference· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Developing a Community of Videoconference Users
Trevor Woods
2006· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About