A social–ecological approach to assessing and managing poaching by recreational fishers
Bibliographic record
Abstract
Effective conservation depends upon people's compliance with regulations, yet non‐compliance (eg poaching) is often the rule rather than the exception. Poaching is often clandestine and socially undesirable, requiring specialized, multidisciplinary approaches for assessment and management. We estimated poaching by recreational fishers in no‐fishing reserves of Australia's Great Barrier Reef Marine Park ( GBRMP ) by conducting social surveys and quantifying derelict (lost or discarded) fishing gear. Our study revealed that (1) between 3–18% of fishers admitted to poaching within the past year, (2) poaching activities were often concentrated at certain times (holidays) and in specific places (poaching hotspots), and (3) fishers’ primary motivations to poach were the perception of higher catches in reserves and a low probability of detection. Our results suggest that extolling certain ecological benefits of marine reserves where enforcement capacity is low could lead to the perverse outcome of encouraging non‐compliance. Our combined social–ecological approach revealed that even in an iconic marine park such as the GBRMP , poaching levels are higher than previously assumed, which has implications for effective management.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".