Investigating the “Leaky Pipeline” Effect: Time-to-Completion Disparities Among Visible Minority Women in Canadian Engineering
Bibliographic record
Abstract
The “Leaky Pipeline” describes the attrition of underrepresented groups in STEM. While U.S. research suggests that Asian students may progress more quickly due to cultural expectations and the Model Minority Myth, this dynamic remains underexplored in Canada. This study hypothesizes that visible minority women, particularly Asian women, face socio-cultural pressures and gendered expectations that accelerate degree completion while introducing unique academic challenges. Using Statistics Canada data, literature analysis, and an original survey, the study examines how gender, ethnicity, and immigration status intersect to influence doctoral timelines. Preliminary findings show that visible minority women have significantly shorter time-to-completion than non-minority women, despite reporting comparable levels of support. Supervisor support and academic stress emerged as key influences, while immigration-related challenges appeared more closely tied to residency status than ethnic identity. The findings highlight the need for culturally responsive mentorship and targeted support for students facing immigration-related barriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".