Establishing and Sustaining SoTL: The Role of Brokering and Strategic Leadership
Bibliographic record
Abstract
Although the scholarship of teaching and learning (SoTL) has continued to evolve as a field, building and sustaining SoTL within educational institutions remains challenging. Through interviews with 18 SoTL scholars, we sought to examine the question, “How do individuals and institutions sustain SoTL?” Our findings highlighted the important roles of brokering and strategic leadership. Using the landscape of practice as our theoretical framework, we identified the many ways that SoTL scholars influence and are influenced by these roles. SoTL practices are undeniably vulnerable to fluctuating institutional agendas and discourses, changes in leadership, funding opportunities, formal rewards, and overt recognition of SoTL, including scholars’ positions and titles. We found institutional actions may be critical in launching SoTL, supporting it at different times, or establishing academic positions for SoTL scholars who are able to promote the field. However, institutional support waxed and waned across institutions, with a few exceptions, which meant that the SoTL brokers working across the landscape of practice sustained SoTL over time through their strategic leadership. Despite varying contexts, our participants were in it for the long haul, brokering their connections to withstand the vagaries of institutional support. Thus, building SoTL is a long-term effort that requires resilience and a certain degree of institutional support, but more importantly, sustained, bottom-up support for faculty development, collaboration, and commitment across the landscape of practice.
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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 | 0.040 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| 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".