"Pick Me, Pick Me, I Want to Be a Counsellor": Assessment of a MEd-Counselling Application Selection Process using Rasch Analysis and Generalizability Theory.
Bibliographic record
Abstract
The purpose of this research project was to evaluate the effectiveness of the Many-Facet Rasch Model and Generalizability Theory as applied to the application selection committee for the Masters of Education in Counselling Program at UNBC. These two models investigated the items used to score applicants and assessed the rater characteristics of each member on the application selection committee. This evaluation was used to inform the School of Education and provide feedback to refine the selection process in the future. Overall, the applicant selection process at UNBC produced a unitary score that can be used to rank all individuals applying to the counseling program. The 5-point rating scale used to evaluate applicants served as an appropriate measurement tool for assessing applicants. The raters who participated as members on the selection committee were fitting both as groups and as individuals in selecting applicants for the counselling program. To conclude, the Many-Facet Rasch Model and Generalizabiilty Theory served as appropriate measurement tools for describing the details of items, raters, and applicants. --P.ii.
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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 | 0.178 | 0.266 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".