Evaluating limitations of current policies addressing climate change-induced food insecurity: A narrative review in the context of late menarche in African females
Bibliographic record
Abstract
Menarche is a critical developmental milestone signalling the onset of female reproductive function. Food insecurity, induced by climate change, has contributed to irregularities in menarcheal age, which has been linked to potential harmful disease outcomes. Specifically, the incidence of a late menarcheal age has been observed in Africa. Various climate impacts, influenced by existing socio-economic conditions, cause Africa to be disproportionately impacted in the incidence of late menarche in adolescent females. This narrative review aimed to examine existing policies impacting health risks associated with late menarche, that are a consequence of climate change-induced food insecurity. Potential policy solutions included the utilization of renewable energy sources, climate-smart agriculture initiatives, and social cash transfer programs. These policies were appraised relative to the African context; barriers to successfully implementing these policies were found such as misalignment of governance objectives, limited financial evaluation, lack of contextual considerations during policy design, and the inability to foresee unintended consequences. These insights highlighted the importance of contextual factors, trade-offs, and contingencies when creating such policies and were used to inform suggested future directions for policy frameworks.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".