Conceptualizing SoTL: Situating One Research-Intensive University into a Broader 4M Framework
Bibliographic record
Abstract
The conceptualization of the Scholarship of Teaching and Learning [SoTL] has evolved over its 30-year history. This study sought to understand how faculty, staff, and students at a research-intensive institution in Ontario, Canada label and describe SoTL. We performed an environmental scan that consisted of: 1) mining academic journal titles to identify names commonly used to describe systematic inquiry into teaching and learning; 2) a campus-wide survey of faculty, staff, and students; and 3) interviews with select faculty members who perform SoTL work. We identified several dichotomies between the findings from these three methods and discuss the meanings of these findings in relation to the 4M, micro-meso-macro-mega levels, framework.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.012 | 0.056 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".