Motivations and sensitivities surrounding the illegal trade of sea turtles in Costa Rica
Bibliographic record
Abstract
Illegal wildlife trade can threaten biodiversity and economic development. Criminal enterprises may add wildlife products to their list of illicit goods by using established trade routes, networks, and individuals. On the Caribbean coast of Costa Rica, killing of sea turtles and removal of their eggs is commonplace. However, beyond conservation NGOs reporting evidence of illegal take, little is known about this activity. Through semi-structured interviews with law enforcement, community members, NGOs, and illegal harvesters, alongside anecdotal information and observations, we aimed to understand the motivations for illegal take. To cross-reference these findings, we assessed sensitivities surrounding illegal harvesting by asking the general public sensitive questions using the randomized response technique; a method used to elicit sensitive information whilst insuring the anonymity of respondents. We included a questionnaire to establish if differences in demographics affected the probability respondents would admit to a turtle-related crime. Our findings identified a rare example of illegal extraction of a wildlife product driven by motivations that were not exclusively livelihood based. We found the majority of illegal take was undertaken by relatively few individuals, dependent on narcotics. The most cited reason for illegal take was that turtle eggs could be used to procure drugs. Law enforcement was under resourced, and informants reported that prosecutions were rare. Local people preferred to purchase rather than harvest eggs suggesting the trade is supply-driven. Those interviewed did not generally regard the subject of illegal harvest as sensitive. Low education levels, high unemployment rates, and marginalization of certain groups may increase susceptibility to narcotics. Although substance misuse and addiction appear to drive illegal trade, associated poverty and marginalization may explain why drug dependency is so prevalent in Caribbean Costa Rica. Increased work opportunities and drug rehabilitation programs may assist in reducing illegal take of turtle eggs on nesting beaches.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".