Bibliographic record
Abstract
Despite tremendous gains over the past 30 years, women are still severely underrepresented in engineering and engineering education. In 2009, only 17.8% of the more than 74,000 engineering bachelor's degrees awarded in the United States went to women, down from 21.2% in 1999. Women are currently 12.7% of all engineering faculty, and only 7.7% of full professors in engineering schools (Gibbons 2010). According to the American Society for Engineering Education (ASEE), 69 women have served as dean of engineering at one of the almost four hundred engineering or technology colleges in the United States and Canada that are institutional members of ASEE, and 38 women held that title in spring of 2010. Seven of the 50 largest engineering schools (in terms of bachelor's degrees awarded) are or have been led by women, and one of these institutions (Purdue) currently has its second female dean. The majority of female deans have assumed that role since the turn of the century, with several women appointed dean each year since 2005, and nine appointed in 2009. Of the 31 former deans, half have gone on to other academic leadership roles including provost, vice-president for research, chancellor, and president. Interviews with 21 women deans over the past eight years for profiles in the SWE Magazine have explored their career paths, accomplishments, work/family issues, and leadership style. This paper will focus on the role of mentors, professional society activities, and leadership experience in the career development of female engineering deans. Future leaders may benefit from the experience of these pioneering women.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".