Climate change, malaria, and public health: accounting for socioeconomic contexts in past debates and future research
Bibliographic record
Abstract
Infectious diseases have long been a focal point of climate change impacts research, with malaria prominent among them. Although it is universally acknowledged that malaria transmission is affected by temperature and rainfall, projections of future levels of malaria under different climate change scenarios have been the object of scientific controversy. One underappreciated reason for this is because modeling research has not consistently accounted for the role of socioeconomic factors in malaria transmission. There is now a growing awareness that greater and more explicit discussion about the impact of socioeconomic factors on malaria transmission under climate change scenarios is needed, but this will require deepened multidisciplinary collaboration and greater attention to climate change vulnerability science. In order to address this need and to ensure that that outputs from this research help address the needs of public health, the following activities are suggested: systematic analyses of past events to assess the relative role of climatic and socioeconomic drivers of malaria transmission, the development of a consistent definition of vulnerability, the development of metrics and indicators for the key components of vulnerability to malaria, greater collaboration with stakeholders, and the development of health‐specific climate change scenarios under the shared socioeconomic pathways (SSPs). Finally, researchers should more explicitly detail how their assumptions about future socioeconomic development affect research findings. WIREs Clim Change 2016, 7:551–568. doi: 10.1002/wcc.406 This article is categorized under: Social Status of Climate Change Knowledge > Knowledge and Practice
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".