Detection of Gender-Biased Items in the Peabody Picture Vocabulary Test
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
This study investigated possible gender bias on a vocabulary test, using a method suggested by Andrich and Hagquist to detect “real” differential item functioning (DIF). A total of 443 adult ESL learners completed all 228 items of the Peabody Picture Vocabulary Test (PPVT-IV). The 310 female and 133 male participants were assumed to be of equal competence, corresponding to levels B1 and B2 on the Common European Framework of Reference for Languages. Male participants outscored female participants, possibly due to the multiple-choice format and to the fact that most gender-biased questions favored men rather than women. Finally, our analysis process yielded only seven items out of 228 as showing gender DIF, which is much lower than the numbers reported in the literature for ESL tests. This low figure suggests that the high number of gender-related DIF items reported in previous research might be attributed to the use of DIF detecting methods that do not take into account artificial DIF stemming from the cross-contamination of test items.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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