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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
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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
fundfunder
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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 2 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.

affunlabeled
FLAME
Yao Wu, Martin Ester
2015· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
136
citations
affunlabeled
A Hierarchical Aspect-Sentiment Model for Online Reviews
Suin Kim, Jianwen Zhang, Zheng Chen, Alice Oh
2013· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
126
citations
affno abstractunlabeled
Predicting political preference of Twitter users
Aibek Makazhanov, Davood Rafiei, Muhammad Waqar
2014· article· en· Social Network Analysis and Mining· Computer Science
machine prediction:candidate · noneconsensus · none
99
citations
fundno affno abstractunlabeled
Natural Language Processing for Social Media
Atefeh Farzindar, Diana Inkpen
2015· article· en· Synthesis lectures on human language technologies· Computer Science
machine prediction:candidate · noneconsensus · none
93
citations
affno abstractunlabeled
A Dataset for Detecting Stance in Tweets
Saif M. Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, Colin Cherry
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
80
citations
affunlabeled
Sentiment Lexicons for Arabic Social Media
Saif M. Mohammad, Mohammad Salameh, Svetlana Kiritchenko
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
70
citations
affunlabeled
Sentiment Analysis of Social Issues
Mostafa Karamibekr, Ali A. Ghorbani
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
58
citations

How this was built: Screen · Findings · About