Institutional transparency improves public perception of lab animal technicians and support for animal research
Bibliographic record
Abstract
The use of animals in research is controversial and often takes place under a veil of secrecy. Lab animal technicians responsible for the care of animals at research institutions are sometimes described as performing 'dirty work' (i.e. professions that are viewed as morally tainted), and may be stigmatized by negative perceptions of their job. This study assessed if transparency affects public perceptions of lab animal technicians and support for animal research. Participants (n = 550) were randomly assigned to one of six scenarios (using a 3x2 design) that described identical research varying only the transparency of the facility (low, high) and the species used (mice, dogs, cows). Participants provided Likert-type and open-ended responses to questions about the personal characteristics (warmth, competence) of a hypothetical lab technician 'Cathy' and their support for the described research. Quantitative analysis showed participants in the low-transparency condition perceived Cathy to be less warm and were less supportive of the research regardless of animal species. Qualitative responses varied greatly, with some participants expressing support for both Cathy and the research. These results suggest that increasing transparency in lab animal institutions could result in a more positive perception of lab animal researchers and the work that they do.
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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 | 0.024 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".