The Authenticity Scale: Psychometric properties of a French translation and exploration of its relationships with personality and well-being.
Bibliographic record
Abstract
In this article, we examined the psychometric properties of a French translation of the Authenticity Scale created by A. M. Wood, P. A. Linley, J. Maltby, M. Baliousis, and S. Joseph (2008, The authentic personality: A theoretical and empirical conceptualization and the development of the authenticity scale, Journal of Counseling Psychology, 55, 385–399). In the first study (N 188), we assessed the reliability of this translation as well as its construct validity and discriminant validity from the Big Five personality traits. In the second study (N 437), we conducted a confirmatory factor analysis to confirm the factorial structure found in Study 1 and the factorial structure obtained by Wood et al. (2008). The results show that the psychometric properties of the French Authenticity Scale are comparable to those of the original English version. The scale has both good scale score reliability ( .77 to .82) and temporal stability over an 8-week period (r .54 to .69). It is based on a three-factor solution explaining 60% of the variance. The scale shows discriminant validity from the Big Five personality traits and is related to both subjective and psychological well-being. This article provides a valid and reliable instrument in French that measures dispositional authenticity and will be useful to clinicians, counselors, and researchers.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".