Accelerating Animal Replacement: How Universities Can Lead — Results of a One-Day Expert Workshop in Zurich, Switzerland
Bibliographic record
Abstract
This report is a result of an interdisciplinary workshop held at the Collegium Helveticum in Zurich, Switzerland in February 2024, in which ideas for accelerating NAMs (New Approach Methodologies) in Swiss universities were shared and discussed. Due to regional differences in university organisation and funding structures, not all recommendations will be transferable to all regions worldwide. All participants were qualified to contribute to the discussion, due to their knowledge and experience of the Three Rs, in particular with regard to their implementation. The workshop participants believed that universities, which play a pioneering role in so many other areas, should also exploit their innovative potential in the field of animal-free research. The workshop uncovered four areas that would need to be addressed in order to achieve a significant change in university science culture and do more justice to the Three Rs, namely: language - innovative framing (pro-replacement framing in official university statements); knowledge transfer - communicating innovative findings in teaching (redirecting curriculum); change of values within science faculties; and structured implementation and well-coordinated planning of the transformation (establishment of a 'transition unit'). Specific strategies for implementing these four areas are outlined. In addition, we discuss why the replacement of animal testing should be an essential goal for universities, why this goal has not yet been achieved, and why concerted efforts toward change are required.
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 | 0.028 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".