Editors' introduction to Where learning deeply matters
Bibliographic record
Abstract
2022 marks the 50th anniversary of McMaster University’s teaching and learning centre, presently known as the Paul R. MacPherson Institute for Leadership, Innovation and Excellence in Teaching (MacPherson Institute or MI), one of the first established in Canada. In alignment with the institute’s vision of “cultivating an environment where learning deeply matters and teaching is valued and recognized by the collective McMaster community” (Paul R. MacPherson Institute for Leadership, Innovation and Excellence in Teaching, 2019, p. 3), we wondered how we might leverage this milestone to further value and recognize teaching and learning at McMaster. This edited volume emerged in response. Composed of 24 chapters written by 86 authors—including undergraduate and graduate students, alumni, current and former staff, teaching assistants, sessional instructors, faculty, and retired faculty—and supported by 46 research participants and 31 peer reviewers, this collection responds to the following questions: How has teaching and learning evolved and changed at McMaster University over time? What have been defining moments of teaching and learning development at McMaster? What must we remember and learn from this history? What critical challenges do we face in teaching and learning today and into the future? How have these emerged, and how might we address them? What are our visions for the future of teaching and learning at McMaster? In this editors' introduction, we review the organization of the book and reflect on intentions, themes, and limitations of the work as a whole.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.037 |
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".