Ebola vaccine innovation: a case study of pseudoscapes in global health
Bibliographic record
Abstract
Global vaccine development is driven by logics that can run counter to local understandings, needs, and contexts. In a global industrial complex, dependent on financial market logics that prioritize private enterprise, highly promising innovative health products developed in public laboratories may be shelved and revealed only when market opportunities attract investment interest. Such an opportunity arrived with the WHO declaration of a Public Health Emergency of International Concern in 2014. The West African Ebola outbreak mobilized a global platform to accelerate development of existing experimental vaccines. This article explores pseudo-authorship by industry of the rVSV-ZEBOV Ebola vaccine that disappeared its actual discovery by public scientists. It shows that vaccine development in a global health pseudoscape disguises the source and means of innovation, deflects resources to the private sector, and can hinder sustainability of health systems. Pseudo-collaborations give little credit to those who have done the risky work, and pseudo-capacity building seldom reaches the people on the ground needing general health systems. Pseudo-standards, however, may offer an opportunity to free evidence-based gold standards to better respond to public health emergencies.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".