Creating a Space for Acknowledgment and Generativity in Reflective Group Supervision
Bibliographic record
Abstract
Small group supervision is a powerful venue for generative conversations because of the multiplicity of perspectives available and the potential for an appreciative audience to a practitioner's work. At the same time, the well-intentioned reflections by a few practitioners in a room can inadvertently duplicate normative discourses that circulate in the wider culture and the profession. This article explores the use of narrative practices for benefiting from the advantages of group supervision while mindful of the vulnerability that comes with sharing one's work among colleagues. The reflective group supervision processes described were modified from the work of Tom Andersen and Michael White to provide a venue that encourages the creative multiplicity of group conversation while discouraging unhelpful discourses which constrain generative conversation.
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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 | 0.047 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.022 | 0.075 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.003 | 0.041 |
| Research integrity | 0.005 | 0.007 |
| 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".