For Which Boys and Which Girls Are Reading Assessment Items Biased Against? Detection of Differential Item Functioning in Heterogeneous Gender Populations
Why this work is in the frame
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Bibliographic record
Abstract
In gender differential item functioning (DIF) research it is assumed that all members of a gender group have similar item response patterns and therefore generalizations from group level to subgroup and individual levels can be made accurately. However DIF items do not necessarily disadvantage every member of a gender group to the same degree, indicating existence of heterogeneity of response patterns within gender groups. In this article the impact of heterogeneity within gender groups on DIF investigations was investigated. Specifically, it was examined whether DIF results varied when comparing males versus females, gender × socioeconomic status subgroups and latent classes of gender. DIF analyses were conducted on reading achievement data from the Canadian sample of the Programme of International Student Assessment 2009. Results indicated considerable heterogeneity within males and females and DIF results were found to vary when heterogeneity was taken into account versus when it was not.
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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 | 0.001 | 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