Designing a model for predicting quality of life based on personality traits and cultural intelligence among Persian-speaking immigrants in France and Canada
Bibliographic record
Abstract
Le travail statistique a finalement ete effectue avec 317 personnes. Les outils de collecte de donnees etaient l'inventaire de personnalite NEO PI-R, la version abregee du questionnaire de l’OMS sur la qualite de vie et le questionnaire d'intelligence culturelle. Pour l'analyse des donnees, des statistiques descriptives et inferentielles (analyse de correlation, regression et equations structurelles) ont ete utilisees. Les resultats montrent que les traits de personnalite ont une correlation significative avec la qualite de vie. Parmi ces traits, il y a une correlation negative avec le nevrosisme et une correlation positive avec les quatre autres facteurs. Par ailleurs, tous les facteurs de la variable « qualite de vie » presentent une correlation positive significative avec toutes les composantes de l’intelligence culturelle. En ce qui concerne les indices de qualite de l'ajustement du modele final, nous pouvons affirmer que le modele fourni et ses coefficients de regression montrent que ces coefficients expliqueraient avec precision la prediction de la qualite de vie basee sur les traits de personnalite et l'intelligence culturelle.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".