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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.

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

Labels cover 0 of 28 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 28 of 28 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.

aboutno affunlabeled
Two thousand million?
David Crystal
2008· article· en· English Today· Social Sciences
machine prediction:candidate · noneconsensus · none
280
citations
affunlabeled
English as an Asian language
Tom McArthur
2003· article· en· English Today· Social Sciences
machine prediction:candidate · noneconsensus · none
90
citations
affunlabeled
Global English: gift or curse?
Ross Smith
2005· article· en· English Today· Arts and Humanities
machine prediction:candidate · noneconsensus · none
50
citations
aboutno affunlabeled
Tom McArthur's <i>English Today</i>
Kingsley Bolton, David Graddol, Rajend Mesthrie
2009· article· en· English Today· Social Sciences
machine prediction:candidate · noneconsensus · none
17
citations
aboutno affunlabeled
The myth of Canadian English
Jaan Lilles
2000· article· en· English Today· Arts and Humanities
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
No good past Dover
John Edwards
2001· article· en· English Today· Arts and Humanities
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
timeless tolkien [part 2]
Ross Smith
2005· article· en· English Today· Social Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Tolkien the storyteller
R. E. Smith
2006· article· en· English Today· Arts and Humanities
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
#Stayhome: Language in tourism advertisements on Instagram
Guyanne Wilson, Esther Zappe, Jonas Silbermann–Schön, Kai Guilliaume, Rebecca Altwicker, Mariana Tapari +4 more
2021· article· en· English Today· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
A decline in spoken English?
Ralph W. Stewart
2003· article· en· English Today· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Partridge in print and online
Jonnie Robinson
2013· article· en· English Today· Arts and Humanities
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About