Examining Canadian Graduate Students’ Views on Ideal Supervision: A Qualitative Coding Approach
Bibliographic record
Abstract
Research highlights academic, mentoring, and personal characteristics students associate with ideal supervision. The Graduate Student Experience Survey (GSES) invited graduate students from all disciplines to share their views on the qualities and characteristics of ideal supervision. The quantity and diversity responses posed a challenge: How can we systematically analyze textual data from diverse graduate students across campus? In this article, we describe the creation and application of a qualitative coding framework—a systematic method for categorizing and coding textual data—to synthesize 824 student responses to an open-ended survey question. We administered the GSES in 2022 and 2023 and conducted a quantitative content analysis and qualitative interpretation of 993 data extracts. Findings are organized into five categories: personal characteristics, teaching/mentoring, relational trust, professional support, and academic support. This deductive approach to qualitative analysis enabled us to identify trends and patterns in the traits graduate students most frequently associated with ideal supervision. These findings have practical applications: researchers can adapt the qualitative coding framework to analyze textual data, graduate students can use findings to identify suitable supervisors, and university leadership can leverage findings to improve supervisory development.
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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 | 0.042 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".