Inclusive Anti-poaching? Exploring the Potential and Challenges of Community-based Anti-Poaching
Bibliographic record
Abstract
In acknowledgement that the largely (para)militarized approach to anti-poaching has its limitations, alternative approaches to conservation law enforcement are being sought. One alternative focuses on including people from local communities in anti-poaching, what we call inclusive anti-poaching. Using a case study of a community scout programme from southern Mozambique, located adjacent South Africa’s Kruger National Park, we examine the potential of a community scout initiative to move towards a more inclusive and sustainable approach to anti-poaching and conservation. While highlighting its challenges and potential drawbacks, we argue that including local people into conservation law enforcement efforts can help address poaching and problematic aspects of current anti-poaching measures. However, to be a genuine and sustainable alternative, community ranger programmes must be part of a broader shift towards developing local wildlife economies that benefits local communities as opposed to supporting pre-existing anti-poaching interventions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".