A cognitive diagnostic analysis of the Social Issues Advocacy Scale (SIAS)
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
‘What would an ideal social justice advocate look like, and how do our graduates compare?’ is asked by training programs in the helping/health professions (e.g. counselling and psychology, nursing, and education) that have social justice advocacy (SJA) as a core competency. We demonstrate a method for answering this question empirically – cognitive diagnostic modelling (CDM). We used the four dimensions of the Social Issues Advocacy Scale (SIAS; Nilsson, Marszalek, Linnemeyer, Bahner, & Hanson Misialek, 2011 Nilsson, J. E., Marszalek, J. M., Linnemeyer, R. M., Bahner, A. E., & Hanson Misialek, L. (2011). Development and assessment of the Social Issues Advocacy Scale. Educational and Psychological Measurement, 71(1), 258–275. doi:10.1177/0013164410391581[Crossref], [Web of Science ®] , [Google Scholar]) as attributes of SJA, and fit SIAS responses to a CDM of 16 attribute mastery profiles. One-quarter of the sample had a profile suggesting SJA attitudes without action; one-fifth, a profile suggesting monitoring SJA in politics without participation; and one-eighth, a profile suggesting individuals rarely engage in action without SJA attitudes. We also found significant relationships between mastery profiles and degree pursued, degree field, and political affiliation. These results demonstrated the utility of CDM for training program assessment of SJA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.021 | 0.001 |
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