Development and Psychometric Properties of the Greek Personality Adjective Checklist (GPAC)
Bibliographic record
Abstract
This study presents the development and the psychometric properties of the Greek Personality Adjective Checklist (GPAC), a new instrument assessing personality in the Greek population. The GPAC is based on the lexical hypothesis tradition and its theoretical framework was derived from the emic study of the Greek personality lexicon ( Saucier, Georgiades, Tsaousis, & Goldberg, 2005 ). It consists of 94 adjectives measuring six concrete dimensions: Even Temper, Introversion/Melancholia, Prowess/Heroism, Agreeableness/Positive Affect, Conscientiousness, and Negative Valence/Honesty. Results from exploratory and confirmatory factor analyses provided support for a six-factor solution for the structure of Greek personality. Additional results provided empirical evidence for the reliability of the GPAC. The Cronbach’s α coefficients for the six scales ranged between .85 and .95. The test-retest correlation coefficients ranged between .71 and .85. Finally, preliminary results provided evidence of construct validity based on convergence correlations with other personality measures such as the Traits Personality Questionnaire 5 (TPQue5), the Big Five Inventory (BFI), and the 50 Big Five Factor Markers (50 BFFM), as well as other criterion personality measures such as the Eysenck Personality Questionnaire (EPQ). It is concluded that the GPAC is a reliable and valid measure, useful for the assessment of normal personality in the Greek population.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".