Leading Up in the Scholarship of Teaching and Learning
Bibliographic record
Abstract
Scholarship of teaching and learning (SoTL) scholars, including those who are not in formal positions of leadership, are uniquely positioned to engage in leadership activities that can grow the field, influence their colleagues, and effect change in their local contexts as well as in institutional, disciplinary, and the broader Canadian contexts. Drawing upon the existing SoTL literature and our own diverse experiences, we propose a framework that describes institutional contexts in terms of local SoTL activity (microcultures) and administrative support (macro-level) and use it to describe the many ways that SoTL scholars can and do “lead up” to effect change depending on their own context. We conclude by inviting scholars to consider, reflect upon, and experiment with their leadership activities, not only for their own professional growth but also to contribute to the literature in this area. Les professeurs qui font des recherches dans le domaine de l’avancement des connaissances en enseignement et en apprentissage (ACEA), y compris ceux qui n’occupent pas un poste de leadership formel, occupent une position unique pour s’engager dans des activités de leadership qui peuvent faire avancer le domaine, influencer leurs collègues et effectuer des changements dans leurs contextes locaux ainsi que dans les contextes plus vastes de leur établissement, de leur discipline et du contexte canadien en général. En nous appuyant sur la documentation déjà publiée en ACEA et sur nos diverses expériences personnelles, nous proposons un cadre qui décrit les contextes institutionnels en termes d’activités d’ACEA locales (micro-cultures) et de soutien administratif (niveau macro) que nous utilisons pour décrire les diverses manières dont les chercheurs en ACEA peuvent en arriver à effectuer des changements selon leur propre contexte. En conclusion, nous invitons les chercheurs à prendre en considération leurs activités de leadership, à y réfléchir et à faire des expériences, non seulement pour leur propre croissance professionnelle mais également pour contribuer à la documentation dans ce domaine.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.025 | 0.070 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".