What would attract women to forest-based climate action? Learning from decades of female participation in an infant and maternal health system in Indonesia
Bibliographic record
Abstract
Low female participation in community-based forest actions for mitigating and adapting to climate change (i.e., “forest climate actions”) increases gender inequalities and could reduce intervention effectiveness. Factors preventing women’s participation in forestry are well-researched, while factors motivating women’s participation is comparatively lacking. We fill this gap by (i) identifying women’s motivations to participate in communal action in other domains; (ii) analyzing to what extent these motivations exist in forest climate actions; (iii) suggesting how forest climate actions can better motivate women’s participation. Our paper presents an original mixed methods approach using data from two studies in different domains (health vs. forestry), objectives (feasibility study vs. impact evaluation), and data collection approach (key informant interviews vs. standardized surveys). Women’s motivations to participate in Posyandu (Pos Pelayanan Terpadu), a state-run infant and maternal health service system operated mostly by female collaborators (Kader), were contrasted with conditions shaping women’s participation in forest climate actions. Data were collected in the same period (2013–2014) in forested rural areas of Indonesia. We find women are motivated by the following values they find lacking in forest climate actions: (1) altruistic values: improving other’s well-being through Posyandu, vs. limited benefits from forest climate actions; (2) social capital: enhancing own and family’s social status by participating in Posyandu, vs. limited social enhancement through forest climate action; and (3) identity enhancement: increasing own pride and competence when supporting an established organization like Posyandu, vs. no equivalent organization for women in forest climate action. What would attract women to forest climate action? We suggest (1) tangible benefits from forest climate action for women and rural communities, so that women see forests are worth fighting for; (2) respected roles for women in public spheres related to forest climate actions; and (3) self-enhancement opportunities through village-level organizations and good employment opportunities aligned with forest climate actions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".