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Record W3156468794 · doi:10.1080/17441692.2021.1912809

‘It’s not the science we distrust; it’s the scientists’: Reframing the anti-vaccination movement within Black communities

2021· article· en· W3156468794 on OpenAlexaff
Krystal Batelaan

Bibliographic record

VenueGlobal Public Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsYork University
Fundersnot available
KeywordsCognitive reframingDistrustSkepticismPandemicRacismInequalityPolitical scienceGender studiesSociologyCoronavirus disease 2019 (COVID-19)CriminologyEnvironmental ethicsMedicinePsychologyLawSocial psychology

Abstract

fetched live from OpenAlex

The anti-vaxx movement is often associated with conspiracy theories and dismissed as being 'anti-science'. However, scepticism from Black communities must not be read as being 'anti-science', but rather 'anti-scientist' due to endemic racism in medical communities and structural inequalities in healthcare. Since slavery and its aftermath - such as through the case of Henrietta Lacks, and now through the Covid-19 pandemic - the devaluation of Black life has been highlighted through the failure to acknowledge and address health disparities amongst racialised and Black peoples [primarily in the United States]. Although the development of a vaccine is an important step in fighting Covid-19, its development and distribution need to be done so safely and in conjunction with addressing the needs and concerns of Black communities, who have been disproportionately affected by the pandemic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0290.038
Scholarly communication0.0110.011
Open science0.0020.013
Research integrity0.0090.016
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.363
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations49
Published2021
Admission routes1
Has abstractyes

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