Understanding and Building Advisory Relationships: An action research initiative supporting graduate student supervision
Bibliographic record
Abstract
The quality of graduate student- supervisor relationships is important because it impacts students’ learning experience as well as their future career opportunities. Indeed, it often influences the course of students’ research, their identity as future scholars, and their motivation to pursue and complete a graduate degree. Though student-supervisory relationships form a great part of the graduate student experiences and professional growth, explorations of this topic from a graduate student perspective are scarce. This participatory action research workshop presented by graduate students in the Faculty of Education at The University of British Columbia seeks to contribute to filling that gap. It addresses the importance of having a continuous and open dialogue about the expectations and experiences of graduate students and supervisors in regard to this key aspect of graduate students’ life. We warmly invite early career scholars, current graduate students, and experienced and novice Faculty members to participate in this workshop aimed at building greater communication and understanding of supervisory relationships in graduate school. Using arts-based inquiry, this workshop will offer a creative and safe space to reflect, share, and develop together better graduate student-supervisor relationships
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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 | 0.066 | 0.049 |
| 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 | 0.017 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.007 | 0.016 |
| 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".