Fish welfare: a challenge to the feelings‐based approach, with implications for recreational fishing
Bibliographic record
Abstract
Abstract Fish welfare issues are increasingly appearing on social and political agendas and have recently gained prominence in fisheries literature. By focusing on examples from recreational fishing, this paper challenges some of the previous accounts of fish welfare. Issues of concern encompass: (1) the feelings‐based approach to fish welfare; (2) the artificial divide between human beings and nature; and (3) ways in which stakeholders can address fish welfare issues. The different approaches to characterizing the interaction of humans with animals are animal welfare, animal liberation and animal rights. We show that the suffering‐centred approaches to fish welfare and the extension of the moral domain to fish – characteristic of the concepts of animal liberation and animal rights – are not the cornerstone of animal welfare. This, however, does not question the need of fisheries stakeholders to consider the well‐being of fish when interacting with them. There are many ways in which recreational fishing stakeholders can modify standard practices to improve the welfare of fish, without questioning fishing as an activity per se. Examples are choice of gear and handling techniques. Previous accounts have failed to include discussions of the many efforts – voluntary or mandated – pursued by fisheries stakeholders to reduce fish stress, injury and mortality. Progress towards addressing fish welfare issues will be enhanced by avoiding the viewing of humans as ‘non‐natural’ disturbance to fishes and keeping three types of crucial question in separate compartments. The three questions cover the symptoms of good and poor welfare, the conscious experience of suffering, and the ethical attitudes towards animals. Fish biologists should focus on the first question – objective measurement of biochemical, physiological and behavioural indicators – to evaluate whether human interactions with fish impair the latters’ health or prevent them from receiving what they need, if held in captivity.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.081 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".