Social Capital, Community Resilience and Sustainable Forest Governance: A Case Study of the Datuk Sinaro Putih Customary Forest, Jambi, Indonesia
Bibliographic record
Abstract
Sustainable forest management is crucial for addressing environmental challenges and supporting local communities. This study explores the role of social networks as a form of social capital in supporting sustainable forest management in the Datuk Sinaro Putih customary forest. Through an exploratory qualitative approach, data were collected via interviews, participant observation, and document analysis in the Batu Kerbau and Dusun Baru Jambi indigenous communities. The findings reveal that both internal and external social networks foster resilience, promoting forest protection based on local wisdom. Key forms of resilience—relational, normative, and structural—are critical in preventing deforestation and mitigating external pressures such as corporate encroachment and illegal mining. Strengthening these social networks, particularly through partnerships with governments, NGOs, and research institutions, can enhance equitable and sustainable forest governance. The practical implications highlight the importance of replicating local practices to support community-based Sustainable Development Goals (SDGs). This research contributes to the understanding of how social networks in indigenous communities can support sustainable forest management and offers valuable insights for policymakers, researchers, and practitioners involved in sustainable forest governance. ©2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".