Detecting DIF (Differential Item Functional ) Across Gender Groups in the Item of Alexithymia Scale Based on Indigenous Psychology Approach
Why this work is in the frame
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Bibliographic record
Abstract
The aim of this study was to detect the presence of DIF (Differential Item Functional) in a quantitative survey data using alexithymia scale based on gender. Alexithymia scale in this study use the Toronto Alexi scale adapted by Bagby RM, Parker JDA, Taylor GJ in 1994, which has been modified and adapted to indigenous psychology approach. This study uses analysis of DIF (Mantel-Haenszel) as a means of processing the data. The research involves a number of 102 subjects (N = 102) consisted of 66 women and 36 men who registered in a senior high school students in Jogjakarta, Indonesia. Results show that 15 items infected by the DIF from 34 existing items. DIF value is varied, some of items are favorable for men, the rest favorable for women. Items that infected by DIF measure difficulty to find emotion term, physical sensation, tends to analyze than describe. The results will be discussed further.
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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 | 0.005 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.010 |
| 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