A Unified Comparison of IRT‐Based Effect Sizes for DIF Investigations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Several marginal effect size (ES) statistics suitable for quantifying the magnitude of differential item functioning (DIF) have been proposed in the area of item response theory; for instance, the Differential Functioning of Items and Tests (DFIT) statistics, signed and unsigned item difference in the sample statistics (SIDS, UIDS, NSIDS, and NUIDS), the standardized indices of impact, and the differential response functioning (DRF) statistics. However, the relationship between these proposed statistics has not been fully discussed, particularly with respect to population parameter definitions and recovery performance across independent samples. To address these issues, this article provides a unified presentation of competing DIF ES definitions and estimators, and evaluates the recovery efficacy of these competing estimators using a set of Monte Carlo simulation experiments. Statistical and inferential properties of the estimators are discussed, as well as future areas of research in this model‐based area of bias quantification.
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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 | 0.019 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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