The people behind the process: Reflections on the Institutional Quality Assurance Process at McMaster University
Bibliographic record
Abstract
This chapter offers a brief history of the first 10 years of the Institutional Quality Assurance Process (IQAP) at McMaster University. Interviews with two senior administrators and six staff were conducted. These interviews highlight how the participants began their involvement with the Institutional Quality Assurance Process and what changes they have observed over time. The participants’ reflections demonstrate the importance of a reflective and improvement-based approach to quality assurance that focuses on student learning experiences. The interviews also provide insight into the possibilities and challenges that have arisen as IQAP supports have become increasingly embedded within the MacPherson Institute for Leadership, Innovation and Excellence in Teaching. Lastly, the value of rapport in multi-unit partnerships on campus and how quality assurance work centres on enhancing student learning at McMaster is also discussed.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.034 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".