New rights for forest-based communities? Understanding processes of forest tenure reform
Bibliographic record
Abstract
SUMMARY This article reports on findings from a research project, in more than 30 sites in 10 countries in Africa, Asia and Latin America, aimed at analyzing cases where changes in formal tenure rights for forest-based communities had recently occurred or were in process. Though by far largest proportion of the world's forests are owned by the state, over a quarter of forests in developing countries are now owned by or assigned to communities. This suggests, at least in some ways, a marked departure from the historic trend towards centralizing. The project, led by the Center for International Forestry Research in coordination with the Rights and Resources Initiative in 2006–2008, sought to identify issues and concerns from the perspective of socially and economically vulnerable groups that were seeking rights reforms. The objectives were to understand reform processes, particularly the extent to which community rights had improved in practice. This article reports on the analysis of three aspects of the reforms: the broad global trends shaping them, challenges in implementation and outcomes for livelihoods and forests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".