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Record W2918997699 · doi:10.1177/1049732319831040

Moving Metaphors: Shifting Institutional Responsibilities and Evidentiary Boundaries in the Commissioning of Pre-Exposure Prophylaxis for HIV

2019· article· en· W2918997699 on OpenAlexaff
Seran Gee, Antony Chum, Bryan Lim

Bibliographic record

VenueQualitative Health Research · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsBrock UniversitySt. Michael's HospitalYork University
Fundersnot available
KeywordsProject commissioningParliamentGovernment (linguistics)LegislatureContext (archaeology)Human immunodeficiency virus (HIV)House of CommonsPublic relationsSociologyPolitical sciencePublic administrationPublishingLawMedicinePoliticsFamily medicineLinguistics

Abstract

fetched live from OpenAlex

In this article, we investigate how speakers in the U.K.'s House of Commons cited the same legislative context and medical research to arrive at contradictory conclusions regarding the Government's responsibility to fund pre-exposure prophylaxis (PrEP) as an HIV intervention. Because the Government had expressed that it would not comment on institutional responsibilities directly, given the likelihood of a legal challenge in response to the National Health Service withdrawing PrEP from the drug commissioning process, the Government's support of this decision could not be explicitly detailed. Our discourse analytic approach reveals how members of parliament adopted positions in the debate by using distinct metaphorical frames and lexical choices to linguistically encode assumptions that imply contrary interpretations of mutually agreed upon facts. This suggests that the concrete discursive practices used to cite evidence in policy-making discussions, regardless of the quality of the evidence, may have material consequences for evidence-based policy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.425
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.312
GPT teacher head0.517
Teacher spread0.205 · 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 teacher head, 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

Citations2
Published2019
Admission routes1
Has abstractyes

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