SoTL’s Watershed Moment: A Critical Turning Point for SoTL at Ryerson University
Bibliographic record
Abstract
Ryerson University has undergone major restructuring in a short period of time. Since 1993, Ryerson has become a degree-granting institution and expanded its post- graduate degree programs as a means to further its commitment to high-quality education. Ryerson’s change in status and enhanced focus on scholarship provides a watershed moment for the Scholarship of Teaching and Learning at the University. This paper introduces our working definition of SoTL, explains the circumstances leading to the watershed moment at Ryerson, and outlines the necessary steps to entrench SoTL at Ryerson. The paper concludes with a reflection upon the lessons learned from other universities attempting a similar task. Our efforts to advance the importance of SoTL may be misdirected until other researchers and teachers understand the role of SoTL in higher education environments. SoTL is sometimes viewed as an illegitimate form of scholarly activity because it does not always end with a peer-reviewed journal paper. This misrepresentation of SoTL needs to be corrected in order to further advance the scholarship and learning and educational opportunities SoTL provides to students.
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 | 0.032 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.053 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".