Appraising the duality of self-monitoring: Psychometric qualities of the Revised Self-Monitoring Scale and the Concern for Appropriateness Scale in French.
Bibliographic record
Abstract
Revising Snyder’s (1974) original Self-Monitoring Scale, Lennox and Wolfe’s (1984) double-sided approach distinguishes two aspects of self-monitoring: (a) the active, high self-esteem, and extrovert side, measured by the Revised Self-Monitoring Scale (RSMS); and (b) the protective, low self-esteem and anxious side, measured by the Concern for Appropriateness Scale (CAS). This study aims at moving forward the assessment of self-monitoring by providing a valid French translation of these two scales. Six hundred and 34 participants were asked to complete the RSMS and the CAS, as well as other measures to assess construct validity. Distribution, scale score reliability, temporal stability, and factor structure were examined. Using a split-sample procedure, construct validity was also investigated, using several criteria (self-esteem, social desirability, extraversion, openness, trait anxiety, self-consciousness, gregariousness, straightforwardness), including new criteria that provide a more accurate definition of the two underlying constructs. The French RSMS and the French CAS replicate the psychometric properties of the other versions (Bachner-Melman, Bacon-Shnoor, Zohar, Elizur, & Ebstein, 2009; O’Cass, 2000) and appear to be psychometrically robust. Strengths, weaknesses, and potential uses of both scales are discussed.
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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 | 0.012 | 0.032 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".