Can SoTL Generate High Quality Research while Maintaining its Commitment to Inclusivity?
Bibliographic record
Abstract
The Scholarship of Teaching and Learning (SoTL) faces an emerging challenge as it seeks to balance commitments to disciplinary inclusivity and scholarly quality. We undertake a scoping review of 64 articles across three leading SoTL journals to investigate how the literature balances these twin commitments by exploring what questions are being asked, what methods are being used, and how these may be impacting the inferences that are being made within that scholarship. We advocate for a more focused definition of SoTL that can help reinforce its legitimacy within institutional power structures of scholarship, and for partnerships across disciplinary boundaries to be a central pillar of SoTL that is both high quality and disciplinarily inclusive.
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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 | 0.584 | 0.730 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.054 | 0.043 |
| Open science | 0.007 | 0.050 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".