Cool cats and communities: Exploring the challenges and successes of community-based approaches to protecting felids from the illegal wildlife trade
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it