Career paths and building a supportive network for female faculty of color
Bibliographic record
Abstract
We are three female faculty of color[1] who crossed paths while pursuing our academic careers. In this work, we share our personal stories of how our identities evolved into who we are today as female faculty members in mechanical engineering and how our identities might continue to impact our paths later in our careers. The three of us have very different identities, but the common point is that we “chose” our careers in academia. Through this work, we discussed what led to our “choice” to become faculty, connected with each other about the difficulties we faced, and highlighted “missed opportunities” in each of our paths. We want to emphasize that there is more than one pathway to a career in academia. We also want to emphasize the importance of self-reflection and find ways to support other women who may be feeling isolated and excluded in engineering. We see this conference as a great venue to share and gain such support from others. [1] The authors are located in the United States of America, where “people of color” generally refer to all non-White populations. Updated with minor typographical corrections: June 30, 2025.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".