ResourceTenure and Power Relations in CommunityWildlife:The Case of Mkambati Area, South Africa
Bibliographic record
Abstract
Through a case study of Mkambati area, this article analyzes the prospects for community wildlife management (CWM) for communities that neighbor Mkambati Nature Reserve. Two clusters of issues are proposed as being crucial in any community-based resource management situation. The ® rst cluster is centered on the idea of ``resource tenures,’ ’ and the need to locate wildlife in a fuller resource= livelihood=tenure institutional context. The second cluster is centered on power dynamics, the multilayered struggles between diverse sets of actors, and the process through which resource tenures are continuously renegotiated. It is argued that wildlife management must always be seen in these larger contexts, and that the prospects for successful community-based schemes will depend crucially on how wildlife tenure articulates with other resource tenures, on how it impacts on rural livelihoods considered holistically, and on the relationships that exist between local and nonlocal institutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| 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".