Making the “Good” Professor: Does Graduate Mentoring Promote Gender Equality in Academia?
Bibliographic record
Abstract
Mentorship is a critical component of a graduate education and facilitates the process of socialization into the role of professorship. Numerous studies continue to support the idea that mentorship, particularly woman-to-woman mentoring, is essential for overcoming barriers to women’s mobility within male-dominated fields. This study critically examines this assumption through the analysis of 59 qualitative interviews with faculty mentors and graduate students in science, technology, engineering, and mathematics conducted at one Canadian and one American institution. Initially, I explore how mothers in academe are socialized from differing levels to fit into narrowly defined roles as “good” professors. This expands our conceptualization of a motherhood penalty to include more subtle discrimination and illuminates the complexity within which motherhood is embedded in work organizations and reproduced through interaction (including mentorship). By following a comparison of the relational dynamics of women graduate students in same-gender and cross-gender iv mentorships, the overwhelming conclusion is that both men and women as faculty mentors are capable of socializing their students in ways that have potential to transform the academic institution regarding gender equity. Still, many examples of how mentoring alternately functions to perpetuate inequities exist. Finally, a crossnational analysis allowed exploration of institutional contexts and how they influence the ways in which mentors model balance. In contexts where family leave is institutionalized (i.e. Canada), conflict between work and family life should be lessened. Given this assumption, we should see a distinct separation of experiences between Canadian and American academics. In reality, these boundaries are more blurred. This finding implies that despite differences in levels of support formally offered to families through policy initiatives, professional barriers experienced by academics prevent the type of substantive benefits they are meant to afford. In practice, faculty mentors remain wedded to ideal worker models rooted in the masculine work ethics of their professions regardless of institutionalized family policies, thereby perpetuating inequality through mentorship. This, in turn, prevents institutional change. In summary, this study contributes to theoretical models of gendered institutions; advances understanding of the tenacity of gender inequality in academia; and informs university policies related to mentoring practices and workfamily policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".