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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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Vaccine Coverage and Hesitancy
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.

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.

3,295 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.
3,295 works in the cohort · of 4,299,418page 2 of 66

Labels cover 28 of 3,295 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 3,295 of 3,295 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.

afffundaboutunlabeled
Individual determinants of COVID-19 vaccine hesitancy
Philip Gerretsen, Julia Kim, Fernando Caravaggio, Lena C. Quilty, Marcos Sanches, Samantha Wells +4 more
2021· article· en· PLoS ONE· Social Sciences
machine prediction:candidate · noneconsensus · none
214
citations
affno abstractunlabeled
Maternal immunisation: collaborating with mother nature
Arnaud Marchant, Manish Sadarangani, Mathieu Garand, Nicolás Dauby, Valérie Verhasselt, Lenore Pereira +8 more
2017· review· en· The Lancet Infectious Diseases· Social Sciences
machine prediction:candidate · noneconsensus · none
181
citations
affunlabeled
How to deal with vaccine hesitancy?
Juhani Eskola, Philippe Duclos, Melanie Schuster, Noni E. MacDonald
2015· article· en· Vaccine· Social Sciences
machine prediction:candidate · noneconsensus · none
170
citations
afffundaboutunlabeled
“Nature Does Things Well, Why Should We Interfere?”
Ève Dubé, Maryline Vivion, Chantal Sauvageau, Arnaud Gagneur, Raymonde F. Gagnon, Maryse Guay
2015· article· en· Qualitative Health Research· Social Sciences
machine prediction:candidate · noneconsensus · none
168
citations
aboutno affunlabeled
Effective Approaches to Combat Vaccine Hesitancy
Jane Tuckerman, Jessica Kaufman, Margie Danchin
2022· article· en· The Pediatric Infectious Disease Journal· Social Sciences
machine prediction:candidate · noneconsensus · none
140
citations
affno abstractunlabeled
COVID-19 vaccination mandates and vaccine uptake
Alexander Karaivanov, Dongwoo Kim, Shih En Lu, Hitoshi Shigeoka
2022· article· en· Nature Human Behaviour· Social Sciences
machine prediction:candidate · noneconsensus · none
134
citations
affno abstractunlabeled
Barriers to immunization among newcomers: A systematic review
Lindsay A. Wilson, Taylor Rubens-Augustson, Malia S. Q. Murphy, Cindy Jardine, Natasha S. Crowcroft, Charles Hui +1 more
2018· review· en· Vaccine· Social Sciences
machine prediction:candidate · noneconsensus · none
128
citations
affno abstractunlabeled
COVID-19 vaccine hesitancy
Ève Dubé, Noni E. MacDonald
2022· review· en· Nature Reviews Nephrology· Social Sciences
machine prediction:candidate · noneconsensus · none
126
citations

How this was built: Screen · Findings · About