Adaptive Capacity of Indonesian Peatland Communities Facing Resource Loss and Fires Threats: Studies on Purun (Eleocharis dulcis) Craftsmen
Bibliographic record
Abstract
This study investigates the adaptive capacity of peatland communities, specifically purun artisans, in response to the transformation of peatlands into oil palm concessions and the devastating impact of land fires on purun raw materials.Traditional reliance on purun as a primary income source is gradually diminishing, with alternative economic opportunities non-peat based becoming increasingly scarce.Utilizing a descriptive qualitative methodology, this research integrates in-depth interviews, observations, and a comprehensive literature review to examine the resilience of these communities amidst environmental change.One hundred individuals from purun artisan groups at the study site were interviewed to gather data on socio-economic transition and adaptation strategies.The findings suggest the current adaptive capacity of these communities is in a precarious state, indicating a need for interventions to enhance resilience and sustainable livelihoods.This research underscores the pressing issue of peatland conversion and its socio-economic implications, making a significant contribution to the discourse on community resilience and peatland management.It also calls for further exploration into viable alternative income sources and capacity-building strategies to safeguard these communities' future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".