SoTL in the Margins: Teaching-Focused Role Case Studies
Bibliographic record
Abstract
The number of teaching-focused faculty (TFF) continues to increase, raising concerns about opportunities to engage in the Scholarship of Teaching and Learning (SoTL) for academics who are hired to focus on teaching rather than research. Various names for these teaching-focused positions include, but are not limited to: instructional, limited-term faculty; permanent, but not eligible for tenure; equivalent to tenure-track (eligible for tenure); and casual teaching-focused. Regardless of title, TFF face a unique challenge: hired for excellence in teaching and committed to improving teaching and learning, they are often not granted support to engage in professional development or research related to teaching and learning. These and other challenges are associated with their academically marginalized positions. The authors are members of the Advocacy Committee of the International Society for the Scholarship of Teaching and Learning (ISSOTL). This paper builds on a session we offered at the ISSOTL conference in Calgary in 2017 where we invited TFF to contribute narrative examples of institutional SoTL challenges and their strategies for overcoming them. We describe potential solutions to creating institutional cultures that are supportive of TFF engaging in SoTL. We finish by offering recommendations for creating a SoTL teaching-focused community within ISSOTL to provide social and professional support.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".