Bibliographic record
Abstract
Purpose The main purpose of this paper is to critically evaluate community‐based natural resource management as an alternative approach to government stewardship of natural resources. The paper discusses Kenya's experience with community‐based approaches, identifies some of the problems that have been experienced in implementing the approach, and suggests ways of strengthening these approaches to ensure that natural resources are managed more sustainably and efficiently and in ways that generate tangible economic benefits to local communities. Design/methodology/approach The research reported in this paper was undertaken through an extensive review of existing literature, discussions with representatives of local communities and resource managers and personal observations. Findings The paper finds that the community‐based approach to the stewardship of natural resources is a viable alternative to state management and can, if properly implemented, result in more equitable distribution of power and economic benefits, reduced conflicts, increased consideration of traditional and modern environmental knowledge, protection of biological diversity, and sustainable utilization of natural resources. In many cases where the approach has been implemented it has not yielded substantial benefits mainly because of institutional, environmental and organizational factors. The successful implementation of CBNRM projects requires a legal and policy framework that empowers local communities and grants them responsibility and authority for natural resource management. It also requires that an acceptable formula be defined for the sharing of the benefits and responsibilities. Practical implications This paper challenges the stewardship of natural resources by the state and presents arguments in support of a community based approach that prioritizes the livelihood needs of local communities and provides them with strong incentives to conserve and utilize natural resources sustainably. Originality/value This paper is original in applying the principles of community based natural resource management to specific local wildlife and forestry cases in Kenya.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".