Bibliographic record
Abstract
Our reflection begins with our presentation at the 2013 Banff Symposium in the Scholarship of Teaching and Learning where we undertook a critique of the “big tent” metaphor that had thus far characterised much of SoTL’s thinking about its inherent diversity. We acknowledged that as proposed by its originators— Huber & Hutchings (2005) —the “big tent” of SoTL was intended as a capacious space, with room for all who wished to enter. Reflecting on this presentation, we argue that the celebratory big tent with its focus on better teaching and learning may helped SoTL become a more respectable academic enterprise. However, this success has entailed ignoring approaches that often bring into view the challenges of teaching “difficult knowledge” as well as students’ desires to remain ignorant of such knowledge. Now, in Canada at least, we argue the Big Tent must be packed away in order to focus on the messier aspects of teaching and learning. We offer some thoughts on what a decolonizing SoTL might look like.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.051 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".