Towards youth-inclusive strategies for research on climate change and health in sub-saharan Africa
Bibliographic record
Abstract
Globally, young people are effecting significant changes in the field of climate change through various means, such as advocacy, education and awareness campaigns, litigation, innovative solutions, and volunteering. These youth-led initiatives are essential, considering that they will face the long-term health effects of climate change. The need to address disparities in climate and health-related research, policy, and program responses in sub-Saharan Africa (SSA) has never been greater, considering the increasingly pronounced effects of climate change on human health. Within the African continent, where research, policies and programs are predominantly shaped by older people, the inclusion of youth is vital to contribute effectively to the discourse on climate change. In this short communication, we reflect on the limited representation of young people as researchers within the African academy studying the links between climate change and health. We provide a rationale emphasizing the urgent need to build a robust community of researchers that encompasses youth. Our argument advocates for gender-responsive investments in training young researchers in climate change and health to deepen their understanding and address the disproportionate impacts on vulnerable populations. We propose strategies to enhance their meaningful involvement in research and knowledge production in these fields.
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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 | 0.160 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.004 | 0.049 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".