Institutional choice and local legitimacy in community-based forest management: lessons from Cameroon
Bibliographic record
Abstract
SUMMARY Decentralization of forest management has become a common policy globally which has allowed communities to regain rights removed through colonization and central state management of forests. However, socioeconomic and environmental outcomes of such community-based forest management schemes have been mixed. Studies have shown the importance of institutions in influencing the success of these new governance arrangements. Based on an extensive literature review supplemented by qualitative research, using focus groups and semi-structured interviews, conducted in nine villages in the humid forest zone comprising three community forests, this research investigated the successes and challenges from decentralization of forest management in Cameroon. A key constraint on success was the inappropriate institutional structure at the local level with responsibility to manage community forests. Community forest management committees with no internally recognized legitimacy and dominated by local elites had replaced roles once played by traditional authorities. Qualitative research showed that in the humid forest zone of Cameroon, the system of accountability for forest resources, prior to the enactment of community forest legislation, included those with historical traditional cultural authority, in the form of clan or lineage heads, as well as the village chief, a legacy of colonial power. Village chiefs or other members of the village council are also selected on the basis of their good moral character. Community forest management committees that are a hybrid of customary authorities and other representatives of the population chosen following the criteria for local legitimacy may capture the best of historical social regulation and build on it so that the local committee may be seen as being accountable to the local population. Since such hybrid institutions are not without their risks, it is important that these institutions be accountable to a local democratic government to further increase their transparency and accountability. Models of community-based natural resource management that incorporate culturally appropriate requirements of legitimacy and accountability in crafting local institutions may have more success in accomplishing both socioeconomic and environmental goals.
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 | 0.005 | 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 | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".