Self-liking and self-competence separate self-esteem from self-deception using self-report
Bibliographic record
Abstract
Two studies examined the relations between self-esteem and self-deceptive enhancement, using profiles of personality. Study 1 demonstrated that the most popular measure of self-esteem (the Rosenberg Self-esteem Scale [RSES]; Rosenberg,1965) is very similar to Paulhus's Self-Deceptive Enhancement (SDE) questionnaire (1991), using correlation and regression techniques. Tafarodi & Swann's (1995) two-dimensional model of self-esteem revealed that self-liking, and not self-competence, also resembles SDE. Furthermore, a bias toward assessing self-liking rather than self-competence characteristic of the RSES appeared to be responsible for its association with SDE. Study 2 confirmed the findings of Study 1, using a larger sample and more comprehensive measures of personality. Various measures of academic and creative achievement as well as intelligence were also included. Only creative achievement was uniquely associated with any of the scales (self-competence). These findings illustrate that use of the RSES results in the conflation of self-esteem and self-enhancement. Self-competence measures may solve this problem.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".