Exploring the Career Pipeline: Gender Differences in Pre-Career Expectations
Bibliographic record
Abstract
The pipeline theory suggests that increasing the number of women in male-dominated fields should lead to more equality in the labour market. This perspective does not account for differences in the expectations of men and women within the pipeline, which may serve to perpetuate inequities. This study explores the differences in the choice of academic preparation, career expectations, and career priorities of 23,413 pre-career men and women using a large sample of Canadian post-secondary students who are about to embark on their first careers. Our results indicate that, although women are increasingly entering male-dominated fields such as science/engineering and business, they continue to have lower salary expectations and expect a longer time to promotion than their male counterparts. That said, young women in male-dominated fields reported higher salary expectations than those in female-dominated fields. Additionally, young women indicated a preference for beta career priorities (e.g., work/life balance) that are associated with lower salaries, while men indicate a preference for alpha career priorities (e.g., build a sound financial base) that are associated with higher salaries. Our study also found that although women are entering the pipeline for male-dominated fields in greater numbers, it does not necessarily result in more equality for women in the labour market. We conclude that the inequities in the labour market are evident within the pre-career pipeline in the form of gendered expectations. We recommend a number of interventions that might address the expectation gap and therefore improve gender equity in the labour market.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".