Seven Guiding Principles for Building Fellowship in SoTL
Bibliographic record
Abstract
Considering the expansive context of the international Scholarship of Teaching and Learning (SoTL) community, SoTL scholars may wish to reflect and identify what exists for building and nurturing fellowship, and how they may be enacted in practice to benefit others. In this reflective article, we draw on our experiences as International Society for the Scholarship of Teaching and Learning (ISSOTL) Fellows to offer seven guiding principles for building fellowship. These principles include: (1) create space, (2) avoid and resist formal roles, (3) maintain confidentiality and build trust, (4) allow members to self-determine their level of engagement, (5) embrace a group process that is emergent and organic, (6) find congruence in values, and (7) acknowledge the evolutionary nature of the collective. When considered as a meaningful process of community building and engagement, these principles may be useful points of discussion, reflection, and as Felten (2013) states in relation to his Principles for Good Practices in SoTL, to “...articulate a vision of scholarship that enhances, perhaps even transforms, teaching and learning in higher education” (p. 121). Much like Felten, our objective was not to craft a detailed, step-by-step guide for fellowship. Instead, we have provided a series of reference points intended to help people clarify and demystify their own SoTL communities and networks, whether these communities are formal or informal in nature.
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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 | 0.070 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".