Indigenous Peoples’ Perceptions of Their Food System in the Context of Climate Change: A Case Study of Shawi Men in the Peruvian Amazon
Bibliographic record
Abstract
Biodiversity and ecosystem conservation in the Amazon play a critical role in climate-change mitigation. However, institutional responses have had conflicted and complex relations with Indigenous peoples. There is a growing need for meaningful engagement with—and recognition of—the centrality of Indigenous peoples’ perceptions and understanding of the changes they are experiencing to inform successful and effective place-based adaptation strategies. To fill this gap, this study focuses on the value-based perspectives and pragmatic decision-making of Shawi Indigenous men in the Peruvian Amazon. We are specifically interested in their perceptions of how their food system is changing, why it is changing, its consequences, and how/whether they are coping with and responding to this change. Our results highlight that Shawi men’s agency and conscious envisioning of their future food system intersect with the effects of government policy. Shawi men perceive that the main driver of their food-system changes, i.e., less forest food, is self-driven population growth, leading to emotions of guilt and shame. During our study, they articulated a conscious belief that future generations must transition from forest-based to agricultural foods, emphasising education as central to this transition. Additionally, results suggest that the Peruvian government is indirectly promoting Shawi population growth through policies linking population size to improved service delivery, particularly education. Despite intentional Shawi moves to transition to agriculture, this results in a loss of men’s cultural identity and has mental-health implications, creating new vulnerabilities due to increasing climatic extremes, such as flooding and higher temperatures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".