1. Mapping a Mirage: Documenting the Scholarship of Teaching and Learning
Bibliographic record
Abstract
The purpose of the conference session upon which this paper is based was to challenge the notion that evidence of scholarship must be limited to publication in a peer-reviewed journal, and to open the doors for creative thinking about what might constitute evidence of scholarship of teaching and learning. Existing theory around defining scholarship (Boyer, 1990; Glassick, Huber, & Maeroff, 1997; Sorcinelli, 2002) can provide a justification for alternatives, but how can scholarship expressed through teaching or other creative performance be demonstrated? Scholarship of Teaching and Learning (SoTL) scholars in particular may face challenges in documenting their scholarship so promotion and tenure committees can understand its worth.My intent was not to negate the importance of peer-reviewed publications, but to parallel them with other forms of scholarly dissemination that I argue might in some cases have more impact on advancing the field. I also maintain that any understandings of scholarship are both individual and contextual (Baxter Magolda, 1999). The purpose of this summary therefore is not to promote a common definition but rather to challenge the traditional boundaries of understanding. Engagement in scholarship suggests an exchange of ideas, and it is my hope that this article may serve as a starting point for future discussion.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".