Assessing Measurement and Predictive Invariance of the Toronto Alexithymia Scale–20 in U.S. Anglo and U.S. Hispanic Student Samples
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Bibliographic record
Abstract
We collected data from a predominately Anglo American student sample in the Southeastern United States and a predominately Hispanic student sample in the Southwestern United States. Along with an assessment of internal consistency reliability, we examined measurement invariance of the Toronto Alexithymia Scale-20 (TAS-20) using confirmatory factor analysis. We also assessed the predictive invariance of the TAS-20. Results indicate that 2 of the 3 TAS-20 subscales demonstrated satisfactory internal consistency reliability across samples. Items from the TAS-20 subscales demonstrated measurement invariance of the latent means. The relationship between 2 measures of emotional dysfunction and the TAS-20 also demonstrated slope and intercept invariance, indicating equivalent validity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it