A creative approach to participatory mapping on climate change impacts among very young adolescents in Kenya
Bibliographic record
Abstract
Adolescent perspectives are crucially important for developing sustainable solutions to address climate change yet remain overlooked in the literature, particularly in low and middle-income contexts. Kenya is an important context to explore youth climate solutions, as youth constitute the fastest growing population facing climate change-related challenges, such as extreme weather events (e.g., droughts) and issues of water, food, and sanitation security. This manuscript details a methodology for participatory mapping on climate-related issues that was co-developed with Kenyan youth and community-based organizations in Kenya. The aim of this paper is to describe the design of a multi-media participatory mapping tool to identify and address the interconnections between social, health, and environmental well-being with very young adolescents (aged 10-14 years) in six geographically-diverse, climate-affected regions of Kenya (Nairobi, Kisumu, Kilifi, Naivasha, Isiolo, and Kalobeyei Refugee Settlement). The authors describe methods used to develop a strengths-based multi-media participatory mapping approach that combines user-friendly geographic information system (GIS) technology with arts-based methods (dance, drawing, music, video). The aim is to share these methods and process of co-development to inform future participatory mapping approaches with youth climate-related issues.
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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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".