A call for wildlife conservation policy evolution: climate change and community-based natural resource management
Bibliographic record
Abstract
This article examines the evolution of Community-Based Natural Resource Management (CBNRM) in Namibia in the face of accelerating climate change. While CBNRM has historically achieved success in aligning wildlife conservation with community empowerment and economic development, its effectiveness is increasingly constrained by environmental degradation driven by prolonged droughts and shifting climate patterns. Drawing on fieldwork conducted in 2020, the author argues that climate change has become the central challenge to the success of CBNRM programs. Despite this, Namibia's conservancies are actively adapting through innovative responses, including the integration of solar power, water-saving technologies and eco-friendly infrastructure in the ecotourism sector. These adaptations aim to mitigate the environmental impact of tourism while generating sustainable income for local communities. However, significant challenges remain, including funding limitations, maintenance difficulties and internal power imbalances. The paper emphasizes the urgent need for policy evolution to incorporate climate change as a core consideration, in academic discourse and in practice. It calls for sustained investment, equitable benefit-sharing and continuous innovation to ensure that CBNRM can meet its original goals under changing environmental conditions. Namibia's case offers crucial lessons for similar conservation efforts across southern Africa.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".