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Fish welfare: a challenge to the feelings‐based approach, with implications for recreational fishing

2007· article· en· W2096133149 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFish and Fisheries · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWelfareFishingAnimal welfareFeelingRecreationFisheryFish <Actinopterygii>Recreational fishingPublic economicsBusinessEnvironmental ethicsPolitical scienceEconomicsPsychologyEcologySocial psychologyLawBiology

Abstract

fetched live from OpenAlex

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.223
Teacher spread0.203 · 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