An implementation of argument-based validation for assessing college major preferences with a hybrid of Likert-rating and forced-choice formats
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
Choosing a college major is a significant career decision. The College Major Preference Assessment (CMPA) helps with this process by using three rounds of Likert-scale ratings to eliminate majors that are not preferred, followed by four rounds of forced-choice questions to narrow down an individual’s top three choices from a list of 50 options. This study used argument-based validation to evaluate whether the CMPA’s design effectively serves its purpose. Researchers examined the assessment’s claims, inferences, warrants, assumptions, supporting evidences, and rebuttals. Data collected for Psychology and Education majors were analyzed using latent trait models, revealing psychometric qualities that match the goals of each round of assessment. These findings were also confirmed by a separate, independent group. Additionally, the study demonstrates that argument-based validation can be flexibly applied to assessments with mixed formats.
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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 | 0.002 | 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 | 0.002 |
| 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