Climate Change, Vector-borne Disease and Interdisciplinary Research: Social Science Perspectives on an Environment and Health Controversy
Bibliographic record
Abstract
Over the last two decades, the science of climate change's theoretical impacts on vector-borne disease has generated controversy related to its methodological validity and relevance to disease control policy. Critical social science analysis, drawing on science and technology studies and the sociology of social movements, demonstrates consistency between this controversy and the theory that climate change is serving as a collective action frame for some health researchers. Within this frame, vector-borne disease data are interpreted as a symptom of climate change, with the need for further interdisiplinary research put forth as the logical and necessary next step. Reaction to this tendency on the part of a handful of vector-borne disease specialists exhibits characteristics of academic boundary work aimed at preserving the integrity of existing disciplinary boundaries. Possible reasons for this conflict include the leadership role for health professionals and disciplines in the envisioned interdiscipline, and disagreements over the appropriate scale of interventions to control vector-borne diseases. Analysis of the competing frames in this controversy also allows identification of excluded voices and themes, such as international political economic explanations for the health problems in question. A logical conclusion of this analysis, therefore, is the need for critical reflection on environment and health research and policy to achieve integration with considerations of global health equity.
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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 | 0.043 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.014 | 0.102 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.015 | 0.009 |
| 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".