Revealing Moments of Mentorship: Social Exchanges Between a Graduate Research Assistant and a Professor
Bibliographic record
Abstract
This is a longitudinal self-study of a mentoring relationship that emerged and evolved between a graduate research assistant and a professor over seven academic years. We use social exchange theory to explore the evolution of this relationship. Specifically, our focus is a series of nine revealing moments that demonstrate evident learning, a relationship change, or shifts in the currencies of exchange over time. These revealing moments arose from intentional opportunities for scholarly development, personal and relational interactions, and unanticipated occurrences. This study exemplifies the dynamics of a mentoring relationship over an extended time period, thereby responding to the dearth of longitudinal studies of mentoring internationally. Key words: mentoring; social exchange theory; mentoring relationships; graduate education; research assistantships Ce projet de recherche est une étude longitudinale de soi portant sur une relation de mentorat qui a évolué entre un assistant de recherche de cycle supérieur et un professeur au cours de sept années académiques. Nous nous appuyons sur la théorie de l’échange sociale pour explorer cette relation. Plus précisément, nous nous penchons sur neuf moments révélateurs qui démontrent soit un apprentissage évident, un changement dans la relation ou l’évolution des échanges. Ces moments révélateurs ont découlé d’occasions délibérées visant le développement de la recherche; des interactions personnelles et relationnelles; et des événements inattendus. Cette étude illustre la dynamique d’une relation de mentorat au fil du temps, apportant ainsi une réponse au manque, à l’échelle internationale, d’études longitudinales portant sur le mentorat. Mots clés: mentorat, théorie de l’échange sociale, relation de mentorat, études supérieures, assistanat à la recherche
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".