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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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Digital Marketing and Social Media
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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.

1,878 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.
1,878 works in the cohort · of 4,299,418page 13 of 38

Labels cover 2 of 1,878 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 1,878 of 1,878 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
Online consumer decision support
Zhenhui Jiang, Weiquan Wang, Izak Benbasat
2005· article· en· National University of Singapore· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
venueno affunlabeled
Brand and Brand Strategies
Yakup Durmaz, Hatice Vildan Yaşar
2016· article· en· International Business Research· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
The ties that bind? Online musicians and their fans
Kate Daellenbach, Rachael Kusel, Michel Rod
2015· article· en· Asia Pacific Journal of Marketing and Logistics· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Internet Marketing and SMEs
Daniel John Doiron
2012· book-chapter· en· IGI Global eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Impact of Social Media in Coffee Retail Business
Ersoy Ayse Begum, Yavuz Keceli, Kwiatek Piotr
2020· article· en· Journal of Business and Economic Development· Social Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
I’ll laugh, but I won’t share
Seung Hwan Lee, Alan Brandt, Yuni Groff, Alyssa Lopez, Tyler Neavin
2017· article· en· Journal of Research in Interactive Marketing· Social Sciences
machine prediction:candidate · noneconsensus · none
10
citations
afffundunlabeled
Reddit in the Time of COVID
Veniamin Veselovsky, Ashton Anderson
2023· article· en· Proceedings of the International AAAI Conference on Web and Social Media· Social Sciences
machine prediction:candidate · noneconsensus · none
10
citations
venueno affunlabeled
The Print Media Convergence: Overall Trends and the COVID-19 Pandemic Impact
Марина Шерешева, Lyudmila Skakovskaya, Елена Брызгалова, Антон Антонов-Овсеенко, Helen Shitikova
2021· article· en· Journal of risk and financial management· Social Sciences
machine prediction:candidate · noneconsensus · none
10
citations
venueno affunlabeled
ECA: An E-commerce Consumer Acceptance Model
Tiziana Guzzo, Fernando Ferri, Patrizia Grifoni
2014· article· en· International Business Research· Social Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Trust building in wine blogs: a content analysis
James D. Doyle, Louise A. Heslop, Alex Ramírez, David Cray, Anahit Armenakyan
2012· article· en· International Journal of Wine Business Research· Social Sciences
machine prediction:candidate · noneconsensus · none
9
citations
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