Political and Community Logics of Emergent Disease Vaccine Deployment: Anthropological Insights from DRC, Uganda and Tanzania
Bibliographic record
Abstract
With a growing number of emerging infectious diseases and the rapid development of vaccines during epidemics and pandemics, public health officials at the global and national level have reported concerns about vaccine hesitancy, often attributing this to a problem of misinformation and poor understanding of risk. However, social scientists have found that vaccination perceptions are complex and multi-faceted. By focusing on the historical, cultural and political influences that affect vaccine acceptance, as well as social justice questions that examine the fair distribution of vaccines, we explore the political and community logics of vaccine deployment using a case study approach. We found differing logics depending on the vaccine and the context and argue that political and community logics come to the forefront during outbreaks as vaccine strategies often are imposed—in different ways—by the Global North. We suggest that, prior to the development and deployment of new vaccines for emergent diseases in the Global South, political level and community logics must be acknowledged and engaged with.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".