Learning to work together: conceptualizing doctoral supervision as a critical friendship
Bibliographic record
Abstract
Faculty supervision has been identified as a critical component of doctoral student socialization in both the higher education and physical education literature. Nevertheless, few faculty members receive explicit training for supervisory roles, and few published scholarly articles discuss the process through which faculty members develop supervisory practices. Drawing from occupational socialization theory, and adopting self-study of teacher education practices as a methodology, the current study sought to understand how Kevin, a faculty member in physical education, developed, articulated, and enacted what it meant to be a student-centered doctoral supervisor while navigating the power dynamics involved in supervision. Kevin was in his second year in a tenure-track faculty position at the beginning of the study, and was in the process of taking on additional roles related to doctoral supervision. Tim, a faculty member at a different university with experience supervising doctoral students, served as Kevin’s critical friend. The dataset included Kevin’s reflective journal and critical friend conversations with Tim, which were analyzed in reference to key turning points. Kevin came to frame doctoral education as a form of critical friendship, which he defined as including three key elements: (a) finding a balance when supporting students, (b) maintaining social relationships with students, and (c) giving up control and allowing students to struggle. The results of this study highlight the difficulties and benefits of critically examining one’s own practice in the context of doctoral supervision and provide recommendations for others who engage in supervisory roles.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.015 | 0.046 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".