Gender Differences in Organizational Commitment among Early Career Engineers in Canada
Bibliographic record
Abstract
To assess a possible explanation for persistent gender inequalities in engineering, this study examines gender differences in recent Bachelor of Engineering graduates’ intention to look for another engineering job three years after graduation. Applying organizational commitment theories, we examined gender differences in job and family characteristics, and feelings of these graduates towards their jobs to understand what underlying factors make these graduates look for a job with another employer. Based on logistic regression analyses of the National Graduates Survey 2013 (Statistics Canada, 2013), we found no statistically significant gender differences in intentions to leave. This indicates that job commitment is unlikely to be the reason for women’s underrepresentation in the occupation. However, women are more likely to look for a job with another employer when they feel overqualified for the work they are doing, are supervising someone at a job, are a visible minority, or when they have children. Moreover, significantly more visible minority men than white men are looking for a new job. These results have implications for the existing retention initiatives for women and visible minority engineers in Canada
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".