Formulating Popular Policies for Peat Restoration Based on Livelihoods of Local Farmers
Bibliographic record
Abstract
Important peatland issues developed were how to restore peatlands and followed by increasing rural livelihoods. This research aimed to analyze how peatlands can be utilized to alleviate poverty? and how to integrate peatland restoration with poverty alleviation. This research has been conducted in peatlands of OKI district, South Sumatra Indonesia in 2017. Data about bio geophysical aspects of peatlands, social, economic and political institutions of farmers were surveyed in the fields, performed in qualitative and quantitative approach, and analyzed in forms of tables and descriptions. Important themes have been discussed in formulating popular policies for peat restoration based on livelihoods of local farmers, among others poor groups; characteristics of farmers from the socio-political aspect; concept of peatland restoration and other lessons-learnt; compatibility of peat-based poverty alleviation; and need to improve policy making. The chronic poor sites tend to overlap with peatland degradation; it is more important to cultivate peatlands to prevent farmers from falling into deeper poverty than to reduce farmers out of poverty, and the intrinsic quality of peatlands and their contents tends to conflict with poverty alleviation goals, but there are some possible trends to minimize peatlands degradation and to alleviate poverty simultaneously. The best approach is to apply the 'win-lose' or 'lose-win' approach, even though we are not able to avoid peatland degradation at a zero level, but at least it can be inhibited. Cooperation between investors and farmers in managing peatlands is needed, so that the peatland resources are not completely degraded.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".