Stakeholder preferences for pangolin conservation interventions in south‐east Nigeria
Bibliographic record
Abstract
Abstract The overexploitation of biological resources severely threatens many species, requiring urgent and effective conservation interventions. Such interventions sometimes require governance structures that incorporate pluralist perspectives and collaborative decision‐making, especially in complex, multi‐faceted and multi‐scale issues like the illegal trade in pangolins. We used Q‐methodology to provide evidence to inform interventions for pangolin conservation in south‐east Nigeria. We sampled stakeholder groups associated with pangolin use and protection, including hunters, wild meat traders and Nigeria Customs Service employees, to elicit their opinion and knowledge on the use and perceptions of pangolins and their preferences for interventions to reduce pangolin decline. We found that the local consumption of pangolin meat as food is the primary driver of poaching in the region. This contradicts popular opinions that pangolins are specifically targeted for international trade, revealing an opportunity for site‐level behaviour change interventions. The different stakeholder groups identified awareness‐raising campaigns, law enforcement, community stewardship programs and ecotourism as preferred interventions, whose effectiveness we attempted to assess using reported case studies. We observed different perspectives between people associated with pangolin poaching and use (predominantly those living around pangolin habitats, including hunters and wild meat traders) and those working to protect them (such as conservation organisations and Nigeria Customs Service employees). For example, the first group supported community stewardship programs, while the latter preferred awareness‐raising and law enforcement efforts. This divergence in perspectives underpins the need for a combination of targeted interventions at the site level to engage different stakeholders while highlighting the potential challenges to collaborative decision‐making for species threatened by illegal wildlife trade. Policy implications. Our results stress the importance of targeted and context‐specific conservation interventions. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".