An Investigation of Item Bias in PISA Science Test in Terms of The Language and Culture
Why this work is in the frame
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Bibliographic record
Abstract
In this study, differential item functioning (DIF) analyses of Science items of PISA 2006 tests were carried out between different samplings. Mantel Haenszel (MH), logistic regression(LR) and signed - unsigned area indexes methods were used. The research group of this study consists of the samples of Australia, Canada; England, Turkey. In order to investigate the sources of DIF field specialist opinions were consulted for released multiple choice items of science test. It is observed that as the linguistic and cultural differences increased between countries, the number of DIF items increased. The number of DIF items varied significantly according to the procedure used. There was not consistency according to DIF detecting method in DIF or non-DIF items. Generally; like other results of bias researchs this research indicated that the main possible reasons for DIF is due to differences in translation, curriculum,cultural relevance, linguistic differences across Turkish and English versions of the tests of released items.
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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 | 0.001 |
| 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 | 0.001 |
| 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