Italian Adaptation of the Revised Multicultural Ideology Scale ( <scp>MCI‐r</scp> )
Bibliographic record
Abstract
The rise of migrants from different cultural backgrounds in Italy highlights the need to promote harmonious coexistence between them and the local population. A key factor in addressing this challenge is the level of acceptance in society of a multicultural ideology. An instrument to measure this concept has recently been revised into the Revised Multicultural Ideology Scale (MCI-r). Despite the urgency of adopting this broader ideology in Italy, no adaptation of the current scale has been made in the Italian context. To bridge this gap, our studies aim to adapt the MCI-r scale to the Italian context and assess its predictive validity on variables crucial for positive intergroup relations. We collected data from two distinct samples: one from Prolific (N = 301) and another from Sapienza University (N = 204). Using confirmatory factor analysis, measurement invariance, and convergent and discriminant validity analyses, we investigated the psychometric properties of the scale based on its recent validations in other societies. Furthermore, we tested its predictive validity concerning the quality of contact with migrants and political orientation. Our findings supported a four-factor solution and a higher-order dimension. Additionally, results supported the predictive validity of MCI-r and the superordinate dimension on positive contact with migrants and political orientations.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".