Community Forestry in Theory and Practice: Where Are We Now?
Bibliographic record
Abstract
Community forestry refers to forest management that has ecological sustainability and local community benefits as central goals, with some degree of responsibility and authority for forest management formally vested in the community. This review provides an overview of where the field of community forestry is today. We describe four case examples from the Americas: Canada, the United States, Mexico, and Bolivia. We also identify five hypotheses embedded in the concept of community forestry and examine the evidence supporting them. We conclude that community forestry holds promise as a viable approach to forest conservation and community development. Major gaps remain, however, between community forestry in theory and in practice. For example, devolution of forest management authority from states to communities has been partial and disappointing, and local control over forest management appears to have more ecological than socioeconomic benefits. We suggest ways that anthropologists can contribute to the field.
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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 | 0.039 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.015 | 0.093 |
| Scholarly communication | 0.029 | 0.056 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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".