Gaps and linkages between biometeorological research across the Global South: a call for new efforts to advance biometeorology in the Global South
Bibliographic record
Abstract
Biometeorology research continues to grow and accelerate in terms of productivity (papers produced, studies conducted, etc.) as well as its direct impact on society and policy. Simultaneously, the scientific community is increasingly acknowledging that research has predominantly focused on the Global North. Additionally, work conducted in the Global South often follows extractive practices that primarily advance the careers and scientific knowledge of researchers from the Global North, offering minimal benefit to the communities studied in the Global South. This short communication intends to serve as a call to the biometeorology community to work collaboratively across continents to understand the current knowledge of biometeorology research in the Global South in addition to identifying the gaps, challenges, and opportunities of conducting grounded research in the Global South led by Global South researchers to support societies equitably. Further, we will provide insights from a workshop held in Johannesburg, South Africa on addressing these aforementioned gaps and linkages of biometeorology research in the Global South. Our work will showcase the opportunities and obstacles early career researchers have, face, and overcome to advance the fields of biometeorology and urban climate to be less extractive and equitable in knowledge dissemination across the spectrum of communities and countries addressing climate impacts through biometeorology.
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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 | 0.051 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".