Building Scholarly Communities: Lessons Learned
Bibliographic record
Abstract
This is a synthesis article of the experience of six post-secondary institutions, Kwantlen Polytechnic University, Queen’s University, Ryerson University, Southeast Missouri State University, University of Glasgow, and The Ohio State University (the coordinating institution) that were brought together by the Carnegie Academy for the Scholarship of Teaching and Learning (CASTL ) under the Institutional Leadership initiative entitled Building Scholarly Communities. Over a four year period, these institutions, through the leadership of key individuals and with the support of teaching and learning centres at their institutions, worked to create a culture where the Scholarship of Teaching and Learning (SoTL) would be seen to be as important as the scholarship of discovery (research). In this synthesis article, common themes and unique experiences are identified based on the stories of the individual institutions. As well, challenges and lessons learned are described.
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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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.025 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".