Change and continuity in a teaching and learning centre over 50 years: The MacPherson Institute from 1972-2022 at McMaster University
Bibliographic record
Abstract
McMaster University’s teaching and learning centre, known today as the Paul R. MacPherson Institute for Leadership, Innovation and Excellence in Teaching, opened its doors in 1972 and marked its 50th anniversary in 2022. Based on a review of textual records and guidance from two institute directors, the chapter illustrates how the institute has enacted four strategies for encouraging culture change in teaching and learning: (a) rational planning, (b) social interaction, (c) human problem-solving, and (d) use of political power. We also track how debate about the appropriate role and focus of the teaching and learning centre has caused a pendulum to swing between several poles: (a) between a micro focus on the expressed needs of individual educators and a macro focus on systemic institutional change, (b) between prioritization of local service delivery and an international reputation for innovative research into teaching and learning and (c) between facilitation of change behind the scenes and pursuit of greater recognition. We reflect on why it might be important to know our history, particularly the problematic capitalist and settler colonial interests that initially funded the institute, and what this history teaches us. We end by amplifying the importance of vision, focus, and acting on areas of historical neglect as the institute plans for the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".