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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.

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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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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.

34 results · 1 filter active ·
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20142025
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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.
34 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 34 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 34 of 34 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 Future of Ubiquitous Learning
Marcelo Fabián Maina, Kinshuk Kinshuk
2015· book· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
72
citations
affno abstractunlabeled
Innovations in Smart Learning
Elvira Popescu, Kinshuk Kinshuk, Mohamed Koutheaïr Khribi, Ronghuai Huang, Mohamed Jemni, Nian‐Shing Chen +1 more
2016· book· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
62
citations
affno abstractunlabeled
Open Education: from OERs to MOOCs
2016· book· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · open_scienceconsensus · none
52
citations
affno abstractunlabeled
New Models of Open and Distributed Learning
Stephen Downes
2016· book-chapter· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
affno abstractunlabeled
Smart Learning for A Sustainable Society
Chutiporn Anutariya, Dejian Liu, Kinshuk Kinshuk, Ahmed Tlili, Junfeng Yang, Maiga Chang
2023· book· en· Lecture notes in educational technology· Psychology
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Challenges and Solutions in Smart Learning
Maiga Chang, Elvira Popescu, Kinshuk Kinshuk, Nian‐Shing Chen, Mohamed Jemni, Ronghuai Huang +1 more
2018· book· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Resilience and Future of Smart Learning
Junfeng Yang, Dejian Liu, Ahmed Tlili, Maiga Chang, Elvira Popescu, Daniel Burgos +1 more
2022· book· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Learning and Teaching with Social Media
Jon Dron, Terry Anderson
2014· book-chapter· en· Lecture notes in educational technology· Psychology
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
An Approach to Measure Coding Competency Evolution
Vive Kumar, Kinshuk, Thamaraiselvi Somasundaram, Steve Harris, David Boulanger, Jérémie Seanosky +2 more
2014· book-chapter· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Story-Based Virtual Experiment Environment
Ming-Xiang Fan, Rita Kuo, Maiga Chang, Jia-Sheng Heh
2014· book-chapter· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Annotation Recommendation for Online Reading Activities
Miao-Han Chang, Maiga Chang, Rita Kuo, Fathi Essalmi, Vive Kumar, Hsu-Yang Kung
2018· book-chapter· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Frontiers of Cyberlearning
J. Michael Spector, Vive Kumar, Alfred Essa, Yueh‐Min Huang, Rob Koper, Richard A. W. Tortorella +2 more
2018· book· en· Lecture notes in educational technology· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Breadth and Depth of Learning Analytics
David Boulanger, Jérémie Seanosky, Rébecca Guillot, Vive Kumar, Kinshuk Kinshuk
2016· book-chapter· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Students’ Science Process Skills Diagnosis
Ming-Xiang Fan, Maiga Chang, Rita Kuo, Jia‐Sheng Heh
2014· book-chapter· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Open Educational Resources in Mongolia
Amartuvshin Amarzaya, A. Bulgan, B. Burmaa, David Porter, L. Munkhtuya
2020· book-chapter· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · open_scienceconsensus · none
0
citations

How this was built: Screen · Findings · About