A Collaborative Self-Study: Reflections on Convening a SoTL Community of Practice
Bibliographic record
Abstract
Communities of practice (CoPs) can provide opportunities for diverse and inclusive groups to convene, share, collaborate, and support others. Using a self-study research approach combined with a visual research method, this study explores both scholarly and practice-based insights to describe the anticipated attributes of a high functioning CoP for the support of collaborative engagement in Scholarship of Teaching and Learning (SoTL). The following nine emergent attributes are identified: 1) Structures; (2) Social environments; (3) Diversity; (4) Knowledge, learning and ideas; (5) Support; (6) Shared leadership; (7) Risk; (8) Results and impact; and (9) Growth over time. This study contributes to the growing body of knowledge related to the value of visual research methods in collaborative self-study. Moreover, the results of this self-study deepen understanding about the practice and role of convenors and organizers of a grass-roots, campus-wide SoTL CoP initiative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".