Mentorship for New Faculty Members: A Qualitative Follow-up to Prior Canadian Research
Bibliographic record
Abstract
Faculty members play a critical role in upholding the academic mission of institutions of higher education and are integral in supporting student success. It is widely understood that new faculty struggle and as incoming cohorts of new faculty become increasingly diverse, their unique challenges are beginning to be studied. This requires that new faculty receive appropriate support as they transition into academia. Mentorship, originally examined through the traditional dyad model has long been seen as the solution that benefits the mentee, the mentor and the institution: the benefit triad. That said, recent research has begun to question the benefit triad and alternative mentorship models are being studied. The gap in the Canadian higher education literature, particularly of cross discipline mentorship initiatives and their long-term implications, led to this study. Thus, the purpose of this study is to build upon prior research to contribute to a qualitative retrospective exploration of mentorship. This qualitative study interviewed 46 faculty members (new faculty and chairs of departments) to gauge their reactions to prior research findings and explore their perspectives to see what if anything has shifted over time regarding mentorship practices. Overwhelmingly participants were not surprised by the summary of previous findings, suggesting that not much has changed. The implications of this research, as viewed through the conceptual framework of this study ethics of care/caring democracy, are that faculty members need to be cared with via mentorship and other initiatives throughout their transition especially amidst the rising pressures of the neoliberal institution.
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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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".