Ispitivanja skala COMPIN-10 i SUCOMP-10
Bibliographic record
Abstract
This study introduces the short Inferiority Complex (COMPIN-10) and Superiority Complex (SUCOMP-10) scales. Participants (N = 4,010; 57% women), aged between 18 and 77 years (M = 29.68, SD = 10.62), were recruited from nine countries and completed the scales online in their native languages. The reliability, dimensionality, and convergent validity of the scales were examined. Satisfactory reliability coefficients were confirmed for both scales. The unidimensional structure of the COMPIN-10 scale was supported across country samples, whereas the SUCOMP-10 scale did not exhibit a unidimensional structure. Additionally, the results indicated that the COMPIN-10 scale only achieved loading invariance, while the SUCOMP-10 scale lacked invariance across countries. The inferiority scores correlated negatively with self-esteem measures, extraversion, agreeableness, and conscientiousness, and the superiority scores correlated positively with self-esteem measures, extraversion, and conscientiousness, confirming the convergent validity of both scales in the respective country samples. The results of this multi-country study indicate that the COMPIN-10 scale is a more robust research instrument; however, further revision and refinement of both scales is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".