EIDs and the Intersectional Health/Livelihoods Paradox in the Rural Global South
Bibliographic record
Abstract
This article presents the framework of the intersectional health/livelihoods paradox to analyse how political economic processes incur land use change to create vulnerability to infectious disease, but that in contending with these risks rural people negotiate conflicts with livelihoods. The conflicts and trade-offs people make in deliberating over health and livelihood outcomes because of ecological degradation are distributed unevenly through lines of social difference, such as gender and class. While the health/livelihoods paradox is evident within contexts of vulnerability to infectious disease, it is poignant when considering the impacts of interventions and containment strategies to control outbreaks in rural settings. Despite considerable attention on the urban context of disease surveillance, spread and containment due to the Covid-19 pandemic, this article refocuses analysis of the impacts of emerging infectious disease (EID) in rural contexts. The article shifts attention away from analysis of the problematic practices of rural households that undertake livelihood activities such as harvesting of wildlife for consumption, to a nexus between land use change, ecologies, livelihoods and health. The literature is fragmented in terms of the landscapes explored, developmental processes, species dynamics, diseases and social contexts. Therefore, this article presents a framework that enables complex dynamics such as these, that lead people to make compromises between competing health and livelihood outcomes to be examined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".