Idealism, conflict, leadership, and labels: Reflections on co-facilitation as partnership practice
Bibliographic record
Abstract
In this reflective essay we explore the process of ‘walking the walk’ of partnership by reflecting on our experiences of working in student-academic/student-student partnership to co-facilitate workshops on ‘students as partners.’ This partnership co-facilitation took place in the context of the Summer Institute on ‘Students as Partners,’ a four-day event organized by colleagues at McMaster University in Canada. Our involvement in the Summer Institute (SI) arose from Kelly’s link to the conference organizers, who invited her to lead workshops along with students. Our essay includes an explanation of who we are and how we co-facilitated in terms of process and delivery, a collective description of our process of reflection, our individual reflections on the experience of cofacilitating, and an analysis of themes that cut across our reflections. These themes—idealism, conflict, leadership, and labels—illuminate the challenges and opportunities of partnership as a model for co-facilitation.
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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 | 0.038 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.080 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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".