Centering community-based knowledge in food security response and climate resilience in southern Madagascar
Bibliographic record
Abstract
Objective Increasingly unpredictable shifts in climate are triggering public health crises globally. Southern Madagascar is particularly vulnerable to climate impacts, despite contributing to only 0.2% of global emissions. Though endemic in Madagascar, climate impacts such as below average rainfall have increased the severity of droughts, putting over half of the population in southern regions at risk of being food insecure in 2022. The following review examines: How can interventions surrounding the current food emergency in southern Madagascar center community-based knowledge in their strategies? Through a social-ecological approach, this review aims to holistically discuss the complexity of the climate and food crises in this region, which is a topic that has not been widely covered in published review articles thus far. Methods We took a comprehensive and social-ecological approach by analyzing research pertaining to the impacts of colonial history, politics, economy, and culture on the current climate, ecology, and food systems of southern Madagascar. Main findings Many current strategies to mitigate climate impacts and food security fail to incorporate community-based knowledge, leading to inequitable and ineffective interventions. Researchers who prioritize historical and cultural context illustrate how local knowledge may serve as a protective factor against climate impacts. Conclusions As climate shifts exacerbate public health crises, aid organizations must center community perspectives in their interventions to foster equitable and sustainable outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".