A French adaptation of the Affective and Cognitive Measure of Empathy (ACME-F).
Bibliographic record
Abstract
Vachon and Lynam (2016) recently introduced a new measure of empathy, the Affective and Cognitive Measure of Empathy (ACME). Besides assessing the traditional dimensions of cognitive and affective empathy, the ACME includes an affective dissonance scale that covers "antiempathy," an important feature of the construct with prominent predictive value not included in other empathy measures. The aim of this study is to provide data on the French version of the ACME. A sample of 851 community-dwelling participants (59.4% female) completed online the ACME questionnaire along with other measures of empathy, dark and pathological personality traits, and aggression. The original ACME bifactor exploratory structural equation modeling structure (i.e., the three empathy dimensions of Cognitive, Affective Resonance, and Affective Dissonance with positive and negative wording items as method bifactors) was successfully reproduced with the French version. Furthermore, these scales displayed satisfying internal consistency coefficients, as well as good item properties according to Classical Test Theory. Convergent validity indices were also similar to those reported for the original English version, and scale scores reached full invariance across gender and proved to be partially invariant across language when comparing the present data to those from the original validation study. The French version of the ACME is well aligned with the original English version and offers a valuable alternative to French researchers and clinicians interested in measuring the various dimensions of empathy. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.000 | 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) | 0.002 | 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 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".