“Let’s say it wears an Ebola Coat, but it’s not Ebola”: The Rhetoric and Politics of Reframing a Vaccine for a Transnational Clinical Trial
Bibliographic record
Abstract
Focusing on how disease, health and vaccine research take on different forms, meanings and interpretations in diverse contexts, we examine the use of rhetoric to recruit people living with HIV in sub-Saharan Africa for an Ebola vaccine clinical trial. Conducted after the West African Ebola outbreak in a country that had not been affected by Ebola, the urgency, relevance and materiality of disease, health and biomedical research takes on different shapes, meanings and understandings. The limitations of multilateral initiatives to address inequalities in associated healthcare and access to essential medicines and vaccines highlight the tensions created when neither local researchers nor patient communities have been involved in the design or planning of the trial, and when the pathologies targeted by experimental technologies are either inappropriate for the people they are aimed at, or unfold without the knowledge of a social consensus. By deciphering the metaphorical discourse on an Ebola vaccine candidate and the erasure of a viral ontology from the hybrid technology to which it gives rise, we understand that the discourse of clinic staff makes it possible to establish a scientific truth in the service of instrumental productivity: manufacturing consent and recruiting arms for vaccine shots. In this article, we show that the closure of biomedicine to an esoteric discourse reflects the weakness of science in communicating what it actually does and the techniques it produces. It also addresses the failure of community engagement in the field of emerging infectious diseases.
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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 | 0.068 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.073 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".