Relationship Matters: Duo-narrating a Graduate Student/Supervisor Journey
Bibliographic record
Abstract
The student/supervisor relationship may be one of the most important aspects of graduate student success; yet, few academics receive any training in student supervision. Students may not know what qualities and capacities to consider when choosing a supervisor. The purpose of this paper is to duo-narrate the story of our experiences with a Social Justice and Equity Studies master’s thesis project at a Canadian University focusing on student/supervisor relationship-building. In this collaborative endeavor we share stories of our experiences in an effort to communicate the centrality of “relationship” in the student/supervisor collaboration. We provide personal and literature-based insights into graduate supervision, including lessons learned and advice(s) for both faculty and students about: 1) relationship matters; 2) the value of communication; and 3) the importance of culture and context.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.025 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.013 |
| 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".