Estimating classification consistency of screening measures and quantifying the impact of measurement bias.
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
Screening measures are used in psychology and medicine to identify respondents who are high or low on a construct. Based on the screening, the evaluator assigns respondents to classes corresponding to different courses of action: Make a diagnosis versus reject a diagnosis; provide services versus withhold services; or conduct further assessment versus conclude the assessment process. When measures are used to classify individuals, it is important that the decisions be consistent and equitable across groups. Ideally, if respondents completed the screening measure repeatedly in quick succession, they would be consistently assigned into the same class each time. In addition, the consistency of the classification should be unrelated to the respondents' background characteristics, such as sex, race, or ethnicity (i.e., the measure is free of measurement bias). Reporting estimates of classification consistency is a common practice in educational testing, but there has been limited application of these estimates to screening in psychology and medicine. In this article, we present two procedures based on item response theory that are used (a) to estimate the classification consistency of a screening measure and (b) to evaluate how classification consistency is impacted by measurement bias across respondent groups. We provide R functions to conduct the procedures, illustrate the procedures with real data, and use Monte Carlo simulations to guide their appropriate use. Finally, we discuss how estimates of classification consistency can help assessment specialists make more informed decisions on the use of a screening measure with protected groups (e.g., groups defined by gender, race, or ethnicity). (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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