TITLE Differential Domain Functioning on the Numeracy Component of the Foundation Skills Assessment: Bringing the Context into
Bibliographic record
Abstract
This study introduced and demonstrated a new methodology for item and test bias studies: moderated differential item functioning (DIF). This technique expands the DIF methodology to incorporate contextual and sociological variables as moderating effects of the DIF. The study explored differential domain functioning (DDF), so that the focus of interpretation for this test is on the "domain " rather than the item. This moderated DDF effect was shown in a multiple choice and constructed response provincial assessment that was designed to match a specific mathematics curriculum. Participants were 45,728 fourth graders, 45,022 seventh graders, and 43,525 tenth graders in British Columbia, Canada. Data were narrowed to create four contrast groups of communities that reflect differences in contextual variables: rural low-income, rural affluent, urban low-income, and urban affluent. Gender DDF was explored using a general liner statistical model. After statistically matching males and females on their mathematical ability,
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How this classification was reachedexpand
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 | 0.002 | 0.006 |
| 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) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".