Sustaining Positive Change in the Teaching Scholars’ Online Community of Practice
Bibliographic record
Abstract
In this paper, we emphasize the value of an online community of practice (OCoP) for bringing together faculty from across disciplines to share and leverage their diverse expertise and perspectives. We examine the transition of an interdisciplinary community of practice through the pivot into an online environment for engagement, communication, and collaboration. Through this paper we describe our individual Scholarship of Teaching and Learning (SoTL) projects and how we have navigated these projects within the Teaching Scholars OCoP, as well as our reflections and key learnings that have resulted from this sustained collaboration. We contribute key learnings and online strategies which can inform and be tailored by other academics and institutions who are developing online communities of practice as an approach to sustaining educational leadership and change in SoTL research and practice in diverse and distributed contexts.
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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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".