Has the time come? Public confidence, trust, and expectations for the oversight of invertebrates used in scientific research
Bibliographic record
Abstract
In this study our aim was to undertake the first study to describe differences in confidence, trust, and expectations for the oversight of scientists using animals in research. Participants were presented with one of four treatments using a 2 by 2 design; terrestrial (T; mice and grasshoppers) vs aquatic (A; zebrafish and sea stars) and vertebrates (V; mice and zebrafish) vs invertebrate (I; grasshoppers and sea stars). A representative sample of census matched Canadian participants (n=959), on a 7-point scale, stated their confidence in oversight, trust in scientists and expectation of oversight for invertebrates. Participants’ open-ended text reasoning for confidence and expectations of oversight were subjected to thematic analysis. Confidence in oversight was highest for TV (mean±SE; 4.5±0.08) and AV (4.4±0.08), less for TI (3.8±0.10), and least for AI (3.5±0.08), indicating the absence of oversight decreased public confidence. Four themes emerged to explain participant confidence, centered on: 1) animals, 2) participant, 3) oversight system, and 4) science. Trust in scientists was similar for TV (4.3±0.07) and AV (4.2±0.07), but higher for TV compared to TI (4.1±0.07) and TV and AV compared to AI (4.0±0.06); absence of oversight decreased public trust in scientists. Participants believed invertebrates should receive some level of oversight but at two thirds of that currently afforded to vertebrates. Four primary themes emerged to explain participant expectation: 1) value of life, 2) animal experience, 3) participant centered, and 4) oversight system centered. We conclude that a gap exists between current and public expectations for the oversight of invertebrates which may threaten the social licence to conduct scientific research on these animals.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".