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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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Journal of Information Literacy
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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.

32 results · 1 filter active ·
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20072025
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Categories
Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
32 works in the cohort · of 4,299,418page 1 of 1

Labels cover 1 of 32 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 32 of 32 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.

affaboutunlabeled
Information literacy skills on the go
Alice Schmidt Hanbidge, Tony Tin, Nicole Sanderson
2018· article· en· Journal of Information Literacy· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affaboutunlabeled
Find the gap:
Erin Alcock, Kathryn Elizabeth Rose
2016· article· en· Journal of Information Literacy· Social Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affaboutunlabeled
Information literacy skills on the go
Alice Schmidt Hanbidge, Tony Tin, Nicole Sanderson
2018· article· en· Journal of Information Literacy· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Connecting theory to practice
Kieren Bailey, Michele Jacobsen
2019· article· en· Journal of Information Literacy· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
“Do as I say, not as I do…”
Silvia Vong
2024· article· en· Journal of Information Literacy· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Beyond databases
Dana Ingalls
2018· article· en· Journal of Information Literacy· Social Sciences
machine prediction:candidate · scholarly_communicationconsensus · none
1
citations
affunlabeled
Three shots are better than one
Amy McLay Paterson, Benjamin W. Mitchell, Stirling Prentice, Elizabeth Rennie
2024· article· en· Journal of Information Literacy· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Shaking up story time
Bartlomiej A. Lenart, Carla J. Lewis
2019· article· en· Journal of Information Literacy· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Capturing the big picture
Navroop Gill, Elena Springall
2021· article· en· Journal of Information Literacy· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About