Gathering: Stories of scholarship of teaching and learning at McMaster University
Bibliographic record
Abstract
In this chapter, we gathered and synthesized deeply personal stories from established scholars at McMaster University and beyond on conducting scholarship of teaching and learning (SoTL). In the spirit of learning from one another’s individual experiences and motivations, we utilized collaborative autoethnography to connect the individual to the collective and knit together testimonials representing all six faculties. Specifically, we highlight how the concept of “gathering” can be accessed in several different ways to illustrate (a) how conversations drive curiosity and innovation, (b) how individuals from diverse backgrounds and expertise come together to collaborate and create new and emergent knowledge, and (c) how instructors can support one another to experiment, play, and take risks in a safe environment. We shine a light on how McMaster’s newly released teaching and learning strategy, “Partnered in Teaching and Learning: McMaster’s Teaching and Learning Strategy 2021–2026,” recognizes and promotes several principles and practices that SoTL practitioners at McMaster have been quietly undertaking for some time. Finally, these stories highlight recommendations and paths forward that will get us closer to our goal of achieving teaching excellence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.023 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".