Translation and validation of a Chinese version of the Appearance Schemas Inventory-Revised in Chinese adults
Bibliographic record
Abstract
The current study translated the Appearance Schemas Inventory-Revised (ASI-R) into Chinese (Mandarin) and examined its psychometric properties in Chinese adult women and men. Sample 1 included 400 women and 400 men to examine the factor structure of the ASI-R with exploratory factor analysis (EFA). Sample 2 involved 300 women and 300 men, and the EFA-derived factor structures in Sample 1 of the ASI-R were examined with exploratory structural equation modeling (ESEM), bifactor ESEM (B-ESEM), and bifactor ESEM with correlated uniqueness for negatively worded items (B-ESEM-CU) for both women and men. Results of the EFA identified a 4-factor model in women and a 2-factor model in men. The B-ESEM-CU consistently showed the best model fit. In the B-ESEM-CU, the general factor was well-defined, but the specific factors were not, supporting the use of the global factor to conceptualize the ASI-R for Chinese women and men. Evidence of adequate internal consistency, test-retest reliability, and construct validity of the global factor of the ASI-R was suggested in both women and men. Findings suggest the ASI-R is a useful instrument to measure body image investment in Chinese women and men, specifically using the B-ESEM-CU to understand the dimensionality of the ASI-R.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".