Developing Institutional Leadership for the Scholarship of Graduate Student Supervision: Lessons Learned in a Canadian Research-Intensive University
Bibliographic record
Abstract
Abstract This chapter examines a recently launched initiative for developing institutional leadership for scholarly approaches to and the Scholarship of Graduate Student Supervision (SoGSS) at the University of British Columbia (UBC). This initiative is led by the Dean, Associate Dean, and former Associate Dean of the Faculty of Graduate and Postdoctoral Studies and is supported by a team of National Teaching Fellows and a graduate student. It involves a customized graduate student supervision (GSS) leaders’ cohort within the International Faculty SoTL Leadership Program at UBC. The initiative arose from institutional concerns about quality assurance and strategic supports for the enhancement of GSS in UBC’s multidisciplinary research-intensive context. The following were noted: (1) widespread discrepancies in the ways that GSS (sometimes referred to as mentoring) is being taken up and exercised across campus; (2) lack of strategic leadership for GSS within units and related professional development initiatives; and (3) inadequate faculty assessment and evaluation protocols (e.g., formative for professional development purposes or summative for tenure, promotion and reappointment purposes) for discipline-specific GSS practices.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".