Ethical considerations for research on small‐scale fisheries and blue crimes
Bibliographic record
Abstract
Abstract Crimes at sea—blue crimes—can have devastating impacts on small‐scale fishing communities. Increasing calls to address “blue crimes” demand more research to address the drivers, patterns, actors and impacts of criminal activities in society and the oceans. This research and policy agenda, however, is not without risks as it might impact individual small‐scale fishers and their communities, exacerbate existing inequalities and contribute to the criminalization of small‐scale fishing practices. This paper discusses the risks and ethical challenges faced by a blue crimes research agenda to improve rather than worsen the plight of small‐scale fishers. We identify eight inter‐related ethical considerations: (i) pay attention to context and forms of involvement, (ii) cultivate reciprocal relationships and collaborations, (iii) evaluate and minimize risks, (iv) integrate storytelling and careful listening, (v) challenge reductionism, (vi) represent people, places, and practices carefully, (vii) follow communication ethics and (viii) consider the legal and policy implications. In light of a review of the literature on blue crimes and small‐scale fisheries, we point to the need for ethically grounded research that is committed to reducing the associated burdens on small‐scale fishers and their communities.
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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 | 0.465 | 0.498 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.047 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".