Broadening Participation: Over Ten Years of Outreach Within the IDETC-DED Community
Bibliographic record
Abstract
Abstract A core ethos of the engineering discipline is to tackle large, complex problems of central importance to society utilizing a range of technical knowledge and skills. One major barrier to this goal is the lack of diversity in the discipline, leading to a shortage of the talent pool, reduced capacity for innovation, and it can negatively impact the educational experience of engineering students. To respond to this charge, the Broadening Participation Committee (BPart) of the American Society of Mechanical Engineering’s Design Engineering Division (ASME DED) has conducted a number of activities aimed at fostering a diverse professional community and addressing the needs of people typically under-represented within engineering. This includes professional development workshops, networking sessions, travel funds available for graduate students and postdoctoral scholars, and micro-grants available for parents with young children. This paper discusses the activities provided by the BPart Committee since 2013, as well as the outcomes and additional initiatives that occurred as a result of the BPart Activities. Examples of such activities include seven professional development workshops, three workshop panels, and seven networking receptions. In the workshop participant feedback, we see some effect when there is a female presenter over a male presenter, but this effect appears to be limited. A discussion on future activities of BPart is presented in order to continue to grow and foster this community.
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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 | 0.035 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".