Towards solution-focused graduate supervision: Developing a research-based live simulation for graduate supervisors
Bibliographic record
Abstract
Effective supervision is vital for graduate students growing into their respected professions. Although a Solution-Focused (SF) approach can help research supervisors develop optimal capacities to support students, few training opportunities exist to date. This article describes the collaborative process of developing a live actor simulation (LAS) for supervisors to practice the SF approach at a university in Ontario, Canada. Themes generated from needs assessment surveys and interviews with 81 graduate students/alumni and 33 supervisors informed the development of three graduate student characters. Seven graduate supervisors then participated in six collaborative learning sessions to learn SF techniques and practice with simulation actors playing the student characters. Participant feedback indicated that the LAS appeared authentically emulating real-life situations they experience in graduate supervision. SF techniques were considered valuable in navigating common challenges pertinent to the supervisor-student relationship. Participants also highly valued the reflective opportunity to share concerns around their experiences with peers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".