Publication reform to safeguard wildlife from researcher harm
Bibliographic record
Abstract
Despite abundant focus on responsible care of laboratory animals, we argue that inattention to the maltreatment of wildlife constitutes an ethical blind spot in contemporary animal research.We begin by reviewing significant shortcomings in legal and institutional oversight, arguing for the relatively rapid and transformational potential of editorial oversight at journals in preventing harm to vertebrates studied in the field and outside the direct supervision of institutions.Straightforward changes to animal care policies in journals, which our analysis of 206 journals suggests are either absent (34%), weak, incoherent, or neglected by researchers, could provide a practical, effective, and rapidly imposed safeguard against unnecessary suffering.The Animals in Research: Reporting On Wildlife (ARROW) guidelines we propose here, coupled with strong enforcement, could result in significant changes to how animals involved in wildlife research are treated.The research process would also benefit.Sound science requires animal subjects to be physically, physiologically, and behaviorally unharmed.Accordingly, publication of methods that contravenes animal welfare principles risks perpetuating inhumane approaches and bad science.Scholarly journals can shape the behavior of researchers towards improved science and adherence to ethical standards.Editorial policies demanding open data and improved transparency, for example, encourage increased reproducibility in science [1-3].Likewise, improving editorial standards for the just treatment of laboratory animals has come into sharp focus [4,5].The welfare of vertebrates studied in the wild, however, has received less consideration despite the reality that wildlife research primarily occurs outside the direct supervision of research institutions.Moreover, whereas laboratory animals belong to only a handful of taxa, wildlife research can span physiological and behavioral variation across more than 60,000 vertebrate species [6].Herein, we describe how inattention to the just treatment of wildlife poses a significant problem in research ethics but identify a potentially transformative route towards change.Analyzing data on animal care policies across 206 journals that commonly publish
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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 | 0.303 | 0.511 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".