Educational Development for Quality Graduate Supervision
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Graduate supervisors need ongoing educational development to enhance and develop their supervisory skills. From new supervisors to the experienced ones, faculty members all benefit from gathering to discuss and exchange their experiences and supervision practices. Increasingly, research is focusing on the study of best practices for graduate supervision given the need to enhance the student/supervisor relationship and students’ satisfaction with the quality of supervision. Offering educational development opportunities for graduate supervisors is complicated and needs more attention from universities. This paper aims to shed some light on the role of graduate supervisors, the factors that contribute to a successful graduate supervision experience, the factors that contribute to the complexity of graduate supervision with a discussion of different types of support for a successful graduate supervision and lastly, by introducing the design of a MOOC that focuses on Quality Graduate Supervision to be offered at the University of Calgary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it