Aménagement forestier et participation : quelles leçons tirer des forêts communales du Cameroun ?
Bibliographic record
Abstract
Outre l’aménagement forestier de grandes concessions et celui de forêts communautaires de taille réduite, certaines communes du Cameroun se lancent depuis peu dans l’aménagement durable de forêts qui leur sont rétrocédées par l’État. Ces Forêts Communales représentent un aménagement forestier intermédiaire entre ces deux types de concessions. Un plan d'aménagement est élaboré et une gestion participative doit obligatoirement être réalisée afin de tenir compte des usages et des intérêts des populations locales, lesquelles peuvent voter à l'encontre du maire. L'article cherche à définir si ce nouveau modèle d'aménagement, contribuant à accroître les capacités d'investissement de la commune et au transfert de pouvoir d'un niveau central vers un niveau local, peut constituer un cadre d'une gestion durable et participative de la forêt du Bassin du Congo.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".