Doctoral Supervision in North America: Perception and Challenges of Supervisor and Supervisee
Bibliographic record
Abstract
The completion rate for graduate studies is around 50% in some programs, and several authors suggest that doctoral supervision in a key factor in explaining this. Existing research on doctoral education reveals an uneven international landscape made up of the perceptions of both doctoral students and their supervisors. In the French-speaking North American context, exploration of doctoral supervision practices still remains unchartered. As a part of the first author’s doctoral thesis, interviews were conducted with 20 supervisors and 20 doctoral students from 8 different faculties. The purpose of these interviews was to capture their perceptions and experiences around doctoral supervision, and to explore with them the main issues related to doctoral supervision. Four dimensions for framing doctoral supervision have emerged from these interviews: a) scientific, b) personal, c) administrative and d) professional. Three main issues stretch along a timeline: 1) admission into a doctoral program, 2) mastering of scientific writing, and 3) employability. This study is an attempt to unpack the complexity of doctoral supervision and, in doing so, to construct a shared language for all concerned parties. The overall purpose of the doctoral research is to identify practices that support effective doctoral supervision and reduce the dropout rate.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".