Bibliographic record
Abstract
BACKGROUND: The practice of giving animal research subjects proper names is frowned on by the academic scientific community. While researchers provide a number of reasons for desisting from giving their animal subjects proper names, the most common are that (1) naming leads to anthropomorphising which, in turn, leads to data and results that are unobjective and invalid; and (2) while naming does not necessarily entail some mistake on the researcher's part, some feature of the research enterprise renders the practice impossible or ill-advised. OBJECTIVES: My aim is to assess whether the scientific community's attitude towards naming animal research subjects is justified. That is, I wish to consider whether the practice of naming animal research subjects is good or bad for the purposes of scientific research. METHOD: After reviewing the extant literature, I constructed a list of the main arguments researchers provide for desisting from naming their animal research subjects. I then analysed these arguments, with a view to determining whether they in fact provide good reasons to avoid naming animal research subjects. CONCLUSION: of researchers giving their research animals proper names. This is because the practice usually leads to greater empathy and so to improved animal well-being. This, in turn, leads to better animal science. Thus, the scientific community's attitude towards naming animal research subjects is not justified.
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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 | 0.220 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.156 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".