Navigating the Lows to Gain New Heights: Constraints to SoTL Engagement
Bibliographic record
Abstract
Novice Scholarship of Teaching and Learning (SoTL) leaders making the transition from scholarly teaching to SoTL to SoTL Leadership face many challenges within higher education. Not only does traditional academic culture confine most academics to disciplinary silos, but promotion and tenure requirements encourage faculty members to conduct SoTL work “off the side of their desk,” if at all (Boyer, 1990; Dobbins, 2008; Webb, Wong, & Hubball, 2013). This paper shares some of the findings from a recent study that investigated what constrained educational leaders’ understanding of SoTL while enrolled in a SoTL Leadership program at a Canadian research-intensive university. The paper will also explore implications for the support and enrichment of educational leadership.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".