Articulating A Multifaceted Approach For Promoting Diversity In Graduate Engineering Education
Bibliographic record
Abstract
This presentation is a continuation of a multiyear discussion focusing on encouraging diversity in engineering education, with an emphasis on graduate education.It will provide both a summary of previous years' discussions and an introduction to this year's discussion, in a session jointly sponsored by three ASEE divisions--the Graduate Division, Minorities in Engineering, and Women in Engineering.In previous years we have looked at successful programs and initiatives at a number of institutions, identified important issues for promoting diversity at the graduate level, and elucidated a strategy for promoting diversity, based on a holistic model which can also be applied in industry.Our focus now is on tactics which can be employed to support this strategy, whether by diversity program coordinators, other college and university administrators, groups of faculty and students, external stakeholders such as potential employers, or individuals.
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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 | 0.059 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".