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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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Text Readability and Simplification
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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.

affaffiliation
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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.

268 results · 1 filter active ·
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20002025
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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.
268 works in the cohort · of 4,299,418page 2 of 6

Labels cover 1 of 268 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 268 of 268 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.

afffundunlabeled
Text understanding in GPT-4 versus humans
Thomas R. Shultz, Jamie M. Wise, Ardavan Salehi Nobandegani
2025· article· en· Royal Society Open Science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Assessing the Readability of Stacked Graphs
Alice Thudt, Jagoda Walny, Charles Périn, Fateme Rajabiyazdi, Lindsay W. MacDonald, Diane Vardeleon +2 more
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
A Survey on Text Simplification
Punardeep Sikka, Vijay Mago
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
fundno affunlabeled
Strategies for Arabic Readability Modeling
Juan Piñeros Liberato, Bashar Alhafni, Muhamed Al Khalil, Nizar Habash
2024· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Classification of Textual Genres Using Discourse Information
Elnaz Davoodi, Leila Kosseim, Félix-Hervé Bachand, Majid Laali, Emmanuel Argollo
2018· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
Constituent processing in compound and pseudocompound words.
Taylor Melvie, Alexander Taikh, Christina L. Gagné, Thomas L. Spalding
2022· article· en· Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Content Words and Readability in Students’ Thesis Findings
T. Silvana Sinar, T. Thyrhaya Zein, Rohani Ganie, Tengku Syarfina, Mahriyuni Mahriyuni, Muhammad Yusuf +1 more
2023· article· en· Journal of Curriculum and Teaching· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Pronoun interpretation in Italian
Lydia White, Heather Goad, Guilherme D. Garcia, Natália Brambatti Guzzo, Liz Smeets, Jiajia Su
2024· article· en· Linguistic Approaches to Bilingualism· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About