Gender differences in job searches by new engineering graduates in Canada
Bibliographic record
Abstract
Abstract Background This study addresses gender differences in early career experiences in engineering by examining entry‐level jobs of Bachelor of Engineering (BEng) graduates in Canada. Purpose/Hypotheses The study explored how gender shapes entry into this male‐dominated occupation in the context of the contemporary knowledge economy. I tested four hypotheses: (H1) There are no gender differences in job search duration and pay for BEng graduates in Canada; (H2) women experience longer job search durations than men and less pay than men; (H3) women's job searches are shorter with less pay than men; (H4) women's job searches are shorter and with the same pay as men's. Design/Method The study uses data from Statistics Canada National Graduates Survey (2013), feminist theories, and the Cox proportional hazard (CPH) model. Results I found that in the context of the knowledge economy, gender is a significant predictor of labor market outcomes during early career stages for Canadian BEng graduates. Hypotheses H1 and H2 were not supported. I identified partial support for Hypothesis H3 and complete support for H4. In particular, I found that women were hired sooner than men for their first engineering jobs and were paid the same salary as their male counterparts. Conclusions Based on this study's results, I argue that early career experiences in engineering occupation continue to be defined by the gender of graduates. This paper offers several potential research areas in the field of engineering education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".