Community Forests in Gabon: How Do Local Communities Take Ownership?
Bibliographic record
Abstract
Wildlife is a common asset to which the local community has the right to consume. To achieve sustainable management of this resource, a community forest (CF) with a wildlife vocation has been set up as part of the “Sustainable management of wildlife and the bushmeat sector in Central Africa” project. Given the constraints faced by these community forests (CFs), we conducted a study to assess their governance in Gabon. Our objective was to examine whether their current mode of operation would allow them to survive in the long term, with a view to integrating sustainable hunting practices. To do this, we constructed a SWOT matrix (strengths, weaknesses, opportunities and threats) to determine their strengths and weaknesses, from which we carried out a factorial correspondence analysis (FCA) to identify potentially viable CFs. This enabled us to understand that most of the difficulties encountered by these CFs stem from the low level of appropriation of this concept by local communities, which is due to the low level of intervention by the forestry administration in raising awareness of CF management. This study shows that local communities must first take ownership of how CFs work so that they can better apply their success factors.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".