IMAGES OF THE FUTURE: GENDER AND PORTRAYALS OF FACULTY AND INDUSTRY MEMBERS IN CANADIAN ENGINEERING SCHOOL RECRUITMENT MATERIALS
Bibliographic record
Abstract
Engineering remains a male-dominated profession, despite efforts toward change. It is possible that the images used in undergraduate recruitment materials could influence how an individual might perceive their “fit” within engineering. We considered both the representation and context of individuals shown in images and videos collected from 18 Canadian English-language engineering schools, using content analysis. In this paper, we focus on individuals coded as either faculty or industry members, as they illustrate the future possibilities of a career in engineering in this material. We found an overrepresentation of women faculty and women industry members compared to their population percentages at the schools (faculty) and in the profession (industry). We also found that women professionals of both types were under-represented among those professionals wearing business attire, and women industry members were over-represented among those industry members whose names and credentials were given, in both the images and videos. We encountered a similar overall over-representation of women among student imagery, and suspect that it is the result of intentionally highlighting women within the schools and the field of engineering. However, we believethat context, as well as presence, matters. The underrepresentation of women in some cases is worth examining for the message it may send about the future prospects of a student considering engineering.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".