Moving Metaphors: Shifting Institutional Responsibilities and Evidentiary Boundaries in the Commissioning of Pre-Exposure Prophylaxis for HIV
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.081 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".