Internal and External Validation of the Empathy Toward Animals Scale
Bibliographic record
Abstract
The Empathy Toward Animals (ETA) scale measures two dimensions of animal-directed empathy: (1) Empathic Concern, encompassing the emotional aspects, and (2) Perspective Taking, encompassing the cognitive aspects. Although adapted from an existing measure of human-directed empathy, the original version of the ETA scale has not undergone a comprehensive investigation of its internal and external validity. Nevertheless, it continues to be used in research assessing animal-directed empathy, based on indicators of internal consistency and face/content validity. The current study sought to enhance the evidence for the ETA scale by (1) evaluating construct validity and (2) assessing convergent validity. To accomplish these objectives, a sample of 800 adults was recruited. Construct validity was evaluated using two sample cross-validation techniques to perform confirmatory and exploratory factor analyses, as well as assess internal consistency. Convergent validity was assessed through correlation matrices, t-tests, and a multiple linear regression exploring variables associated with the ETA scale. Results support the reliability of two distinct dimensions (i.e., Empathic Concern and Perspective Taking) and the latent variable (i.e., Empathy Toward Animals), and there were significant associations with conceptual constructs as expected (e.g., human-directed empathy and compassion, attitudes and beliefs about animals and nature, demographic variables). Additionally, human-directed empathy and nature relatedness significantly predict ETA. Implications for the definition and measurement of animal-directed empathy are discussed. The findings highlight the potential of leveraging empathy within interventions aimed at deepening human–animal bonds and promoting pro-environmental behaviors.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".