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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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Sentiment Analysis and Opinion Mining
Retraction
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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.

797 results · 1 filter active ·
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20002025
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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.
797 works in the cohort · of 4,299,418page 15 of 16

Labels cover 0 of 797 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 797 of 797 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
Applications of Social Media Text Analysis
Atefeh Farzindar, Diana Inkpen
2018· book-chapter· en· Synthesis lectures on human language technologies· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Opinion Mining
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
OutdoorSent
Wyverson Bonasoli de Oliveira, Leyza Baldo Dorini, Rodrigo Minetto, Thiago H. Silva
2020· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Sentiment Detection and Analysis
Professor Reda Alhajj, Professor Jon Rokne
2014· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Microblog Sentiment Analysis
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
KERMIT for Sentiment Analysis in Italian Healthcare Reviews
Leonardo Ranaldi, Michele Mastromattei, Dario Onorati, Elena Sofia Ruzzetti, Francesca Fallucchi, Fabio Massimo Zanzotto
2022· book-chapter· en· Accademia University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Traffic Detection using Sentimental Analysis
B Mounica, T Thejas, Syed Nadeem Pasha, K. Sreeshma K.P. Swaraj
2020· article· en· International Journal of Scientific Research in Computer Science Engineering and Information Technology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Exploring Public Trust Through LLM-Driven Opinion Mining
Lily Dey, Fahim Anzum, Ulises Charles-Rodriguez, A. S. M. Hossain Bari, Jean-Christophe Boucher, Aleem Bharwani +1 more
2025· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Sentiment Analysis Applied on Tweets
Yuan Chen, Wang Xianglong, Jiang Xingda, Jiale Wang, Huang Boxuan
2025· article· en· Theoretical and Natural Science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About