Community forest enterprises and social enterprises: the confluence of two streams of literatures for sustainable natural resource management
Bibliographic record
Abstract
Purpose Community forest enterprises (CFEs) represent a unique business model in the forest sector which has significant potential to foster community development through sustainable utilization of forest resources. However, CFEs are mired in numerous management challenges which restrict their ability to harness this potential. This paper identifies those challenges and, by drawing on the field of social enterprises, offers specific solutions to address them. The paper also enriches the social enterprise literature by highlighting the role of decentralized decision-making and community empowerment in achieving sustainable development. Design/methodology/approach Using qualitative meta-synthesis, the paper first identifies key challenges from the CFE literature. It then draws on the social enterprise literature to distill actionable insights for overcoming those challenges. Findings The study reveals how the social enterprise literature can guide CFEs managers in making decisions related to human resource management, marketing, fundraising, developing conducive organizational cultures and deploying performance measures. Originality/value The paper provides novel and actionable insights into managing and scaling CFEs. It also identifies opportunities for future inter-disciplinary research at the intersection of decentralized management of natural resources and social enterprises that could facilitate progress toward achieving sustainable development.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".