The Collaborative to Combat the Illegal Trade in Turtles: Addressing Illegal Wildlife Trade with an Adaptive Socio-Ecological Approach
Bibliographic record
Abstract
Illegal wildlife trade is a complex and lucrative transnational crime that involves social, ecological, cultural, political, and economic factors. It is also a significant conservation challenge that can threaten species, ecosystems, and societies. Although illegal wildlife trade negatively impacts various species, many North American turtle populations are exceptionally vulnerable to the removal of wild individuals due to their life history traits. Some of the key challenges to addressing illegal trade in turtles include shortcomings in laws, regulations, and the criminal justice system; insufficient data to understand the issue; and insufficient resources to combat the issue. Herein, we provide a brief characterization of the illegal turtle trade in North America and describe how a grassroots working group, the Collaborative to Combat the Illegal Trade in Turtles (CCITT), formed in response to this urgent conservation crisis. Our collaborative and adaptive socio-ecological approach includes examples and serves as a case study on how wildlife trafficking can be addressed through identifying the need and scope of the problem, building and expanding a network of core partnerships, defining a strategy, and implementing that strategy in an adaptive and iterative way. Looking ahead, we recognize that the CCITT has gaps in representation and, therefore, a need to expand partnerships as well as work towards the full implementation of our strategic plan. While there will never be a “one-size-fits-all” approach to combating illegal wildlife trade, we maintain that sharing approaches, successes, lessons learned, and outcomes with others outside of the immediate area of focus is critical to advance conservation outcomes.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".