Investigating Graduation and Dropout among Doctoral Students in Canada: A Duration Model Analysis
Bibliographic record
Abstract
This article analyses administrative data on students who entered doctoral programs in Canada between 2011 and 2016, providing new insights into doctoral completion and dropout patterns. Graduation rates within seven years vary significantly, with rates exceeding 60 percent in fields such as the physical sciences, life sciences, and engineering, yet falling below 30 percent in the humanities. I estimate a competing risks duration model that accounts for both graduation and dropout events, incorporating variables such as gender, age, immigration status, field of study, institution, marital status, and family composition. The findings indicate that, while controlling for other factors, female students are significantly less likely to drop out than male students but also less likely to graduate. International students exhibit significantly higher completion rates compared with their domestic counterparts. Additionally, there are significant variations in the likelihood of graduation and dropout across different fields of study, ages, institutions, marital statuses, household arrangements, and number of children.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".