Gender Representation In Architectural Engineering – Is It All In The Name?
Bibliographic record
Abstract
Under-representation of women in engineering is of concern as the decreasing supply of qualified engineers continues to plague the nation's advancement.Understanding what factors influence choices of engineering disciplines has the potential for altering education to accommodate a more diverse student body that can be successful in engineering.University statistics reflect that the Architectural Engineering program at this school is comprised of 35% women, while the other engineering programs attract at best 20% women and at worst 5% women.The Architectural Engineering program at this university is in fact one of the most intense structural engineering programs in the country requiring 203 quarter units to complete and upper division courses in integrated design of buildings using concrete, steel, wood and masonry along with seismic design of buildings.The department is however housed in the College of Architecture and Environmental Design rather than the College of Engineering.This overall research study explores the learning styles of different engineering disciplines and the learning styles preferred by students who select these disciplines as academic majors and careers.The work in progress centers on surveys of students in engineering programs at this university.A preliminary survey of women in the ARCE department was administered to discover why these women personally chose ARCE as a major, why they persist in the major, and why they think women are so largely represented in the major.A more detailed survey will follow from this work which specifically investigates the three components of the integrated learning style taxonomy -motivation, engagement and learning processes of both women and men in the ARCE department.Future work will investigate other engineering disciplines that represent maximum differential in representation of women from that of ARCE at this university as well as engineering programs at other universities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".