Addressing Marine Wildlife Entanglement in Derelict Fishing Nets Using Community-Based Social Marketing: Case Study and Lessons Learnt
Bibliographic record
Abstract
Background Entanglement of marine species, particularly endangered sea turtles and cetaceans, in abandoned, lost or otherwise discarded fishing gear is a major conservation concern. Focus of the Article This case study applies Community-Based Social Marketing (CBSM) to reduce marine wildlife net entanglement in the waters surrounding Masirah Island, a marine biodiversity hotspot in Oman. Importance to the Social Marketing Field The study demonstrates the use of social marketing tools in biodiversity conservation, bringing new knowledge to the cross-application of these two fields. Methods The CBSM methodology was applied to select behaviours, identify barriers and benefits, develop strategies and design a pilot study. The responsible disposal of derelict nets in skip bins was selected as the target behaviour, and a mix of behavioural change tools was applied to achieve change: convenience (installation of three skip bins), education (installation of informative signs, distribution of awareness posters, one-to-one engagement with fishers on the beaches), prompts (installation of signs and posters on vessels) and social norms (one-to-one engagement with key influencers and decision makers). The monitoring of behaviour change took place through structured observations over 23 weeks, focussing on the number of nets disposed of in the allocated skip bins. Results Results showed a low level of behaviour adoption rate by skiff and launch vessel fisheries, respectively, 5.36% and 2.58%. Positive results were observed for a short time but did not reach the estimated target value throughout the study period. Recommendations for Research Our pilot study did not lead to broad-scale implementation and we recommend further awareness and engagement with the target audience, trials of various behaviour change tools and increase field monitoring time. We further recommend the application and funding of behaviour change methods applied to fishers with the incorporation of conventional financial, conservation and regulatory tools to support resource management. Limitations Our results show that focussing on specific behaviours with appropriate measurement is both resource and time demanding to solve pressing conservation problems, particularly ones generated by complex industries such as fishing. Various lessons, useful for other social marketers, have been drawn from our evaluation of the overall study.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".