It Matters Who You Ask: Validity and Reliability of Animal Empathy Scoring Scales in Canadian Public and Participants in Beef Production
Bibliographic record
Abstract
Reliable measurements are central to understanding animal-directed empathy. This research study utilizes data from two online surveys to evaluate the validity and reliability of measures of animal-directed empathy. The survey data was of (1) Canadians who have participated in beef cattle processing events (n = 812), and (2) members of the public from across Canada (n = 668). As a part of these surveys, individuals were asked 22 animal empathy score (AES) questions, and an additional 5 questions about livestock-directed empathy (LES). The AES correlated well with an 8-question short form (AES-SF) previously developed in other studies. Confirmatory factor analysis showed that the AES-SF structure was a good fit within the public responses but did not fit well with the responses of those participating in the beef industry. The reliability of the AES and AES-SF was high in the public population, but low in the population participating in beef cattle production. The LES fit well with the public responses, with high reliability and moderate correlation with AES; however, it did not fit well within the industry participant responses. Overall, the results support the use of AES-SF as a measure of animal-directed empathy within public populations. Measurement of this construct needs further development for individuals working directly with livestock species. Researchers should proceed with caution in using animal-directed psychometric measures validated with public populations, as evidence from this study suggests these measures have poor reliability and validity in populations of individuals working directly with livestock species.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".