Is Difference in Measurement Outcome between Groups Differential Responding, Bias or Disparity? A Methodology for Detecting Bias and Impact from an Attributional Stance
Bibliographic record
Abstract
Measurement bias is a crucial concern for test fairness. Impact (true group difference in the measured scores) is of the ultimate interest in many scientific inquiries. This paper revisits and refines the definitions for bias and impact and articulates a conceptual framework that decouples them from differential item functioning. The conditions for showing bias and impact are articulated and a methodology for empirically detecting them is proposed. The framework and methodology hinges on attributing bias and impact to the studied groups by way of matching on balance scores (e.g., propensity scores estimated by the confounding covariates). A real data demonstration comparing two test-language groups on the mathematics items of TIMSS is provided as a proof of concept and guide for application. In closing, we draw readers’ attention to some caveats and suggestions for adopting this conceptual framework and methodology.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".