A cognitive diagnostic analysis of the Social Issues Advocacy Scale (SIAS)
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".