Spinning our wheels and deepening the divide: Call for an evidence-based approach to the fish pain debate
Bibliographic record
Abstract
There is vigorous ongoing debate about whether fish feel pain and have the capacity to suffer. The body of literature dedicated to the topic is increasing but what is particularly problematic is that the majority of the contributions represent opinion pieces and thus fall within the realm of advocacy. Many of the empirical research papers purporting that fish do or do not feel pain have problems with cavalier use of definitions, poor experimental design, or statistical/technical issues and tend to include advocacy statements in their interpretations. Rather than continuing to spin our wheels and deepen the divide, I would advocate our community undertake a balanced, transparent and rigorous appraisal of all available evidence to help guide us and provide more clarity on pain and suffering in fish. This could be done through the use of evidence synthesis techniques such as systematic review and should be done by a reputable independent body such as a learned society or scholarly organization. Our continued emphasis on littering the peer-reviewed literature with opinion and advocacy is only confusing the matter for the public, media, policy makers and the rest of the scientific community.
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.
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 | 0.443 | 0.541 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.009 | 0.082 |
| Scholarly communication | 0.041 | 0.081 |
| Open science | 0.015 | 0.024 |
| Research integrity | 0.068 | 0.079 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".