Challenges of participation in local forest initiatives
Bibliographic record
Abstract
Local forest initiatives such as Community Forests and Social Forestry have been growing in recent decades to improve community participation and address landscape problems where factors such as poverty and forest degradation interact. Although participation has broadly increased, some communities still struggle to utilize these initiatives to improve forest governance. This thesis aims to address this phenomenon through a social relational approach to resource governance and policy analysis to understand how participation in decentralized forestry processes, as a function of policy context, influences local forest governance. Case studies of different communities in British Columbia, Canada and Indonesia are at different stages of developing and I have examined their local forest initiatives to provide insights on this phenomenon. The study in British Columbia (Chapter 2) focuses on local forest initiatives in Cariboo Regional District and Central Kootenay District to understand the challenges and opportunities of different communities in attempts to establish and manage their community forests. Data were collected through online interviews with governments, non-governmental organizations, and community members. The study in Indonesia (Chapter 3) investigates the implementation of Indonesia’s Social Forestry program and its influence on community participation in Social Forestry processes in Maluku Province. The research presented here reveals that communities will need to navigate through two crucial phases in developing their local forest initiatives to improve governance. In the first phase, social conflicts tend to be more prevalent as communities struggle to manage differences in aspirations and agendas to establish a common vision for their local forest initiative. This is the phase of heightened social conflicts. Local forest initiatives will then naturally transition to an operational stage marked by harvesting, marketing, and selling of their forest products. At this phase communities are likely to benefit from building forest expertise to improve effectiveness of management. Both phases influence the effectiveness of communities’ self-organization in utilizing social or community forests to improve benefits. Throughout each stage, the policy context shapes the way people participate in decentralized forestry processes. Insights from this study can help further research on utilizing community participation for improving local forest decision making.
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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 | 0.042 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".