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Record W3211791911 · doi:10.1177/15245004211053841

Addressing Marine Wildlife Entanglement in Derelict Fishing Nets Using Community-Based Social Marketing: Case Study and Lessons Learnt

2021· article· en· W3211791911 on OpenAlex
Maïa Sarrouf Willson, Craig Turley, Lamees A. Daar, Hussein Samh Al-Masroori, Hussain Al Muscati, Madrak Al Aufi, Asma Al Bulushi, Suaad Al Harthi, Andrew Willson

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Marketing Quarterly · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsSocial marketingFishingWildlifeEndangered speciesInfluencer marketingFisheryEnvironmental resource managementSocial mediaEnvironmental planningBusinessGeographyMarketingEnvironmental sciencePolitical scienceEcologyMarketing management

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.330
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it