The Core Self-Evaluation Scale: Psychometric Properties of the German Version in a Representative Sample
Bibliographic record
Abstract
The Core Self-Evaluation Scale (CSES) is an economical self-reporting instrument that assesses fundamental evaluations of self-worthiness and capabilities. The broad aims of this study were to test the CSES's psychometric properties. The study is based on a representative survey of the German general population. Confirmatory factor analyses were conducted for different models with 1, 2, and 4 latent factors. The CSES was found to be reliable and valid, as it correlated as expected with measures of depression, anxiety, quality of life, self-report health status, and pain. A 2-factor model with 2 related factors (r = -.62) showed the best model fit. Furthermore, the CSES was measurement invariant across gender and age. In general, males had higher values of positive self-evaluations and lower negative self-evaluations than females. It is concluded that the CSES is a useful tool for assessing resource-oriented personality constructs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".