How do Rural Youth Make their Voices Heard in Climate Change Planning in the Andean Communities? A Case Study from the Mantaro Valley, Central Peru.
Bibliographic record
Abstract
This research draws from participatory research in two regions of the Peruvian Andes and provides an analysis of youth voices in climate change planning. The research explores how communities assess and mitigate the impacts of climate change, opportunities and barriers to youth participation and effective strategies to further rural youth engagement in climate change planning. Research methods include focus group discussions, key-informant interviews, and video-based fieldwork. Youth are and will be disproportionately affected by the negative effects of climate change. Nonetheless, their participation in climate change decision-making and high-level discussions are extremely limited. Youth (aged 15-24) account for one in every five people in developing countries and one in every eight in the global North, and their numbers are growing much faster in developing countries than in higher-income countries. Very little research has focused on amplifying the voices of rural indigenous youth in climate change planning. This paper provides a comprehensive understanding of the opportunities and barriers that rural youth face while working and living. Results indicate that the community needs more female youth empowerment, training, and capacity development, creating spaces for youth, university-community partnership, working with high-school youth, and an intersectoral approach to education and social services. Most importantly this research recommends three main tools that can support youth to effectively amplify their voices and representation in planning for climate change in their communities. This includes having a web of support, a great social network and climate-related education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".