Cool cats and communities: Exploring the challenges and successes of community-based approaches to protecting felids from the illegal wildlife trade
Bibliographic record
Abstract
Implementing community-based approaches to countering illegal wildlife trade is important to not only improve the effectiveness of strategies to protect wildlife, but also to promote equity and justice. We conducted an international exploratory review of interventions that aim to address the illegal trade in wildlife using a variety of community-based approaches. We focused our study on Felidae species in particular, as they factor centrally in the illegal wildlife trade, and have received significant conservation attention due to many being charismatic species. We searched for case studies that have been or are currently being implemented, and that were published between 2012-2022 in scholarly or grey literature databases. We extracted data on 40 case studies across 34 countries, including information on the approaches used, successes, challenges, and recommendations using a Theory of Change framework for community action on illegal wildlife trade. Initiatives to protect Felidae species from illegal trade could consider using multi-pronged approaches, consider historically underrepresented groups within communities - including women - in their design, and should evaluate the social and ecological outcomes to improve future efforts.
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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 | 0.045 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".