The Motivation to Empathize Scale - Indexing Virtuous and Nonvirtuous Motives to Empathize
Bibliographic record
Abstract
While recent conceptualizations of empathy have highlighted its motivated nature (eg. (Keysers & Gazzola, 2014; Zaki, 2014) little work has yet explored the specific motivations that influence one’s propensity to empathize. Commonly-used self-report metrics of empathy include items that lean heavily, if not entirely, towards ‘virtuous’ motives (e.g. concern, sympathy, caring, helping), and empathy has been explicitly linked to these motivations in many writings. However, the definition of empathy is silent to its virtuosity; and while rarely indexed, several less virtuous motivations for empathy can be readily identified: to influence, to manage, to mediate, to manipulate. Towards a more thorough investigation of the various motives underlying empathy, the present paper introduces the Motivation to Empathize scale, which was specifically designed to parse one’s propensity to consider the feelings of another into both virtuous (e.g. caring/compassionate/loving) and nonvirtuous (e.g. selfish, manipulative, sinister) motives. The paper outlines initial steps taken towards scale development and item reduction, and provides preliminary evidence of scale reliability and construct validity. Specifically, factor analytic techniques separated empathic motivations into two (high-alpha) factors, with all virtuous motives loading on latent factor one, and all nonvirtuous motives loading on latent factor two. Thus, virtuous and nonvirtuous motives to empathize appear to constitute distinct, and statistically separable, measures of the propensity to empathize. Virtuous, but not non-virtuous motives, correlated with the empathic concern subscale of the Interpersonal Reactivity Index (IRI; Davis, 1980), and each motivation type showed distinct relationships with the Compassion and Politeness aspects of Agreeableness (ie. big-five personality traits). In total, these results suggest that both virtuous and nonvirtuous motives may predict the manifestation of empathy, and that future work would do well to consider these varied motivations when considering the nature of the empathic construct.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".