Bibliographic record
Abstract
Most would agree that animals in research should be spared "unnecessary" harm, pain, or distress, and there is also growing interest in providing animals with some form of environmental enrichment. But is this the standard of care that we should aspire to? We argue that we need to work towards a higher standard-specifically, that providing research animals with a "good life" should be a prerequisite for their use. The aims of this paper are to illustrate our vision of a "good life" for laboratory rats and mice and to provide a roadmap for achieving this vision. We recognize that several research procedures are clearly incompatible with a good life but describe here what we consider to be the minimum day-to-day living conditions to be met when using rodents in research. A good life requires that animals can express a rich behavioral repertoire, use their abilities, and fulfill their potential through active engagement with their environment. In the first section, we describe how animals could be housed for these requirements to be fulfilled, from simple modifications to standard housing through to better cage designs and free-ranging options. In the second section, we review the types of interactions with laboratory rodents that are compatible with a good life. In the third section, we address the potential for the animals to have a life outside of research, including the use of pets in clinical trials (the animal-as-patient model) and the adoption of research animals to new homes when they are no longer needed in research. We conclude with a few suggestions for achieving our vision.
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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".