An Exploratory Strategy to Identify and Define Sources of Differential Item Functioning
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
The sources of differential item functioning (DIF) items are usually identified through a qualitative content review by a panel of experts. However, the differential functioning for some DIF items might have been caused by reasons outside of the experts' experiences, leading to the sources for these DIF items possibly being misidentified. Quantitative methods can help to provide useful information, such as the DIF status and the number of sources of the DIF, which in turn help the item review and revision process to be more efficient and precise. However, the current quantitative methods assume all possible sources should be known in advance and collected to accompany the item response data, which is not always the case in reality. To this end, an exploratory strategy, combined with the MIMIC (multiple-indicator multiple-cause) method, that can be used to identify and name new sources of DIF is proposed in this study. The performance of this strategy was investigated through simulation. The results showed that when a set of DIF-free items can be correctly identified to define the main dimension, the proposed exploratory MIMIC method can accurately recover a number of possible sources of DIF and the items that belong to each. A real data analysis was also implemented to demonstrate how this strategy can be used in reality. The results and findings of this study are further discussed.
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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 | 0.000 | 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