Community Resilience Related to Community Resources Access to Peatland in Political Ecological Perspectives: A Case Study of Purun (Eleocharis dulcis) Craftmen in Ogan Komering Ilir, South Sumatera, Indonesia
Bibliographic record
Abstract
Community resilience is widely used in managing natural resources and the environment as a means of system capacity to cope with stress. However, our findings show that resilience is not easily applied to common-pool resources (CPRs) such as peatlands, which are open access and full of importance. This is experienced by the community of purun craftsmen (Eleocharis dulcis) in Ogan Komering, Ilir Regency, South Sumatra, Indonesia. This paper was conducted to determine the community's social resilience in overcoming pressures originating from environmental, socio-economic, and political changes. We used a qualitative research method with a descriptive approach and obtained data through observation, in-depth interviews, and documentation. Our findings suggest that community resilience on peatlands is influenced by mechanisms to gain and maintain access to the resource. This mechanism is relatively limited, so it can be said that it is less robust, mainly if three threat scenarios co-occur, such as massive activity by companies, weak rules for managing, utilizing, and protecting resources, and extreme weather conditions. In conclusion, from these findings, we show that “access politics” and policy implications also play an essential role in increasing the resilience of socio-ecological systems in important peatland areas.
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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 | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".