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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".