Impact of Inclusion of Varying Percentages of Repeaters on Equating
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
Restricted equating samples are often used to equate test results. Previously eligible students may be excluded because this group of students is not stable from year to year and their inclusion may bias the results. The present study evaluated the impact of including previously eligible students in the equating samples, where the percentage of repeaters varied from 5% to 40% in 5% increments. Whereas including previously eligible students in the equating samples had impact on the results for the equating samples, there was little impact on the equating results for the population. Thus it seems reasonable to include these students in the equating samples, thereby increasing the representativeness of the equating sample. Whether the findings of the study are generalizable to situations where a fixed number of common items are used in all forms to be equated and the time between the two administrations is shorter than a year needs to be addressed in future research.
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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 | 0.004 |
| 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