A good mentor is hard to find: examining the frequency, depth and conditional effects of mentoring relationships in a faculty-in-residence program
Bibliographic record
Abstract
Student-faculty interaction is thought to be an important factor in students’ engagement with their post-secondary institution, but the benefit to students usually correlates with the quality of the relationship. Faculty-in-residence programs have been championed as a way to encourage both intentional and casual out-of-the-classroom interactions between students and faculty. McGill University’s Faculty-Mentor-In-Residence program was designed to provide conditions for meaningful connections between students and faculty to take place, but the frequency and depth of interactions had not been evaluated. Using Cox and Orehovec’s (2007) typology of student-faculty interaction to analyze participant responses, this study sought to determine whether the program increased meaningful mentoring relationships, and for whom. Most participants interacted with faculty, but the students who formed the deepest relationships were white and cis-gendered, while students who hold systemically marginalized identities experienced more superficial interactions.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".