Disentangling Counter‐Empathy: Developing a Three‐Dimensional Model and Measure of Dispositional Counter‐Empathy
Bibliographic record
Abstract
OBJECTIVES: Counter-empathy involves responding to others' assumed emotions incongruently. Research on dispositional counter-empathy predominantly focuses on specific counter-empathic constructs without clearly mapping its cardinal dimensions. We develop and test a Three-Dimensional Model of Counter-Empathy (3DCE) that includes schadenfreude, gluckschmerz, and affective sadism. METHOD: Across five studies (total N = 1878), we test the 3DCE and develop the Various Indices of Counter-Empathy (VICE). Study 1a and Study 1b administered items representing the 3DCE to develop the VICE. Study 2 administered the VICE, measures of counter-empathic constructs, empathy, everyday sadism, and socially aversive outcomes. Study 3a and Study 3b administered vignettes of others' good fortunes and misfortunes, and depictions of general and social harms, and participants reported their reactions. RESULTS: The 3DCE and validity of the VICE are supported by exploratory and confirmatory factor analyses; a "bass-ackward" factor analysis mapping the hierarchical structure of counter-empathy; incremental analyses predicting socially aversive outcomes beyond empathy; correlations with relevant constructs; and predicting counter-empathic reactions to specific scenarios. CONCLUSIONS: The 3DCE and VICE can help situate prior research in the broader structure of counter-empathy, help expand the study of vicarious emotion beyond empathy, and suggest counter-empathy contributes to socially aversive outcomes beyond a lack of empathy.
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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".