The SoTL Body: Identifying and Navigating Points of Entry
Bibliographic record
Abstract
The scholarship of teaching and learning (SoTL) as a field invites researchers to examine their teaching practice with the goal of understanding its impact and effect on student learning (Hutchings & Shulman, 1999). Though inclusive by nature – belonging to no discipline yet informing practice in all – SoTL does have its own discourse, assumptions, and literature that may intimidate disciplinary scholars. This paper uses the human body as a metaphor to explain how researchers from diverse disciplines can use familiar entry points to ease their transition into SoTL. We identify and analyze parts and systems of the human and research body, revealing connections between particular disciplinary research bodies and the SoTL research body – connections that we hope provide disciplinary scholars with the confidence they need to navigate and engage in SoTL.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.002 | 0.026 |
| 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".