Social forestry for a good life? The uneven well‐being benefits of Indonesia's social forestry scheme
Bibliographic record
Abstract
Abstract The Indonesian government has scaled up its devolution of forest management to local communities in the form of social forestry, with the aim of improving forest management while delivering nature‐based well‐being (including livelihood) benefits for local communities. We investigate the varied well‐being impacts of social forestry management rights—an expression of human–nature relationships—and how these are distributed among different social groups in Indonesia. In a study conducted in four different village locations with social forestry management right permits in Indonesia, we employed a mixed methods approach comprising interviews (semi‐structured and life story) ( n = 80), focus group discussions (in‐person and online) ( n = 44) and a survey of 100 households in each site ( n = 400). We identified local conceptualizations of, and priorities for, well‐being. Our findings indicate that access to land and livelihood capitals that support productive and diverse livelihoods is central to well‐being. Good social relations are crucial for securing and utilising land to sustain a livelihood, and in turn, to achieve a good life. Material and social elements underpin other interconnected well‐being dimensions, including being able to perform religious pilgrimages, contribute to cultural and spiritual practices and provide security for one's children's future. Further, we found that social forestry secured land access, improved access to agricultural inputs and information, and diversified livelihoods of those who knew they were involved in social forestry—aligning particularly with material well‐being priorities valued locally. However, disparities in access to information meant that most villagers (67%) in areas granted social forestry permits knew little about the scheme. Active social forestry participants were from more well‐off households, while many less privileged people faced exclusions to participation and benefits. Unclear institutional processes, exclusionary enrolment practices and limited access to information hindered social forestry's potential to contribute to well‐being more broadly, risking increasing social inequalities locally. Policy implications : Our findings suggest the need for measures to ensure well‐being benefits from social forestry are distributed equitably. This is essential to avoid exacerbating social inequalities and to foster greater support for forest protection. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".