Examining the Role of Friendship in Mentoring Relationships between Graduate Students and Faculty Advisors
Bibliographic record
Abstract
Although previous studies have offered empirical and anecdotal support for academic mentoring, there are still considerable gaps in understanding the specific actions or components that are present in these relationships. Research has shown that academic faculty mentors provide all of Kram’s (1988) mentoring functions to their graduate student protégés. Despite numerous claims to the presence of “friendship” in graduate student-faculty advisor mentoring relationships, others question if friendship is even possible within this context. Thus, there is ambiguity about the role of this particular function in academic mentoring. In our attempt to reconcile results from a previous study on graduate student-faculty advisor mentoring and better understand the potential role and temporal development of friendship within this domain, we sought clarification in the existing literature. To our surprise, the literature lacks consensus on the topic and requires additional scholarly attention. Consequently, the purpose of this paper is to share insights from our previous study examining mentoring in academia, summarize empirical findings and conceptual advancements on the topic of friendship in graduate student-faculty advisor mentoring relationships, and propose directions for further inquiry in this area, in the hope of strengthening academic mentoring 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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".