The 3Rs principle – mind the ethical gap!
Bibliographic record
Abstract
Over the 50 years since they were first proposed, the 3Rs (Replacement, Reduction, Refinement) have made a tremendous impact. These principles seem to unify concerns for better science with causing less harm to animals. The ideas behind the 3Rs are so intuitively compelling that it is tempting to believe that full implementation is merely a matter of time, and once the 3Rs are widely implemented, the public will fully support any continued laboratory animal use that is deemed necessary. In this paper, we argue that these conclusions are unlikely to be correct, in part because the 3Rs are rich in ambiguities, and any implementation requires resolving the dilemma that promoting one R will sometimes directly or indirectly conflict with promoting another. For example, should Reduction be conceived in absolute or in relative numbers? Is it really possible (or desirable) to use relative Replacement (i.e., switching from a “higher” to a “lower” species)? Which of the 3Rs should receive priority? Until now, some scholars have focused on identifying Replacements for the use of live animal experiments in research, while others have focused on Reduction in the number of animals used and Refinements in procedures such that animals experience less harm. Meaningful contact between these camps may be limited, however. In some cases, the goals of Reduction and Refinement actually conflict, as, for example, in the choice to re-use animals (and hence reduce total animal usage) or to avoid re-use (and hence avoid the negative effects of repeated exposure to harmful procedures). We conclude that there is now a need for a more thorough ethical discussion on how to resolve these issues.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.115 | 0.019 |
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; both teacher heads 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".