Discovering the Pathways of Engineering Mentorship: An Auto-Ethnographic Exploration
Bibliographic record
Abstract
Mentorship acts as a catalyst for belonging and success in the field of engineering, particularly for people from underrepresented groups. This paper examines the mentorship experiences of a female undergraduate engineering student. Using an auto-ethnographic methodology and mixed-methods approach, mentorship experiences were thematically and statistically analyzed through the lens of gender, mentorship pathway, timing, and impact-identification. Key findings include the presence of mentorship experiences with primarily male faculty, staff, and students prior to beginning an engineering degree, highlighting the important role that men, in all positions, play as allies in the recruitment of girls in engineering. It was found that invitations to more impactful mentorship experiences often developed from an initial experience that the mentee first initiated. Further, the defining attributes of a mentor consisted of being a role model, being welcoming, offering encouragement, and providing guidance. Overall, the exercise of reflecting on mentorship was a powerful experience, highlighting the importance of self-reflection in student development.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".