Relationship between the inferiority and superiority complex and the Big Five and Dark Triad traits
Bibliographic record
Abstract
This research aimed in exploring inferiority and superiority complexes' relationship with dark personality traits . In order to provide a more comprehensive overview personality measures from the Big Five model were included in the study as well. Sample with 1046 respondents completed online versions of the short Inferiority and Superiority Complex scales, Dark Triad Dirty Dozen and BFI-10. Data were analyzed using hierarchical multiple regression . It was found that personality measures from the BFI-10 explain more variance in inferiority and superiority complexes than dark personality traits . Both inferiority and superiority complexes were found associated with Narcissism suggesting a person's focus on own self, psychopathy was positively related only with superiority, while Machiavellianism was not related to any complex. In general, the contribution of the dark traits in explaining inferiority and superiority complex is relatively weak. Obtained relations with personality measure from the BFI were expected; neuroticism showed the greatest predictive power for inferiority and, cconscientiousness and extraversion for superiority complex. Obtained results just partly confirmed presumptions on the role of dark traits on the inferiority and superiority complexes. In the following studies longer versions of the personality traits scales should be used in order to provide more robust findings.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".