Disability and the stratification of post‐secondary pathways: Evidence from a large administrative linkage
Bibliographic record
Abstract
Research has linked disability to differential experiences and outcomes for students at multiple levels of education. To date, however, available data sources have prevented comprehensive analyses of the statistical relationship between disability and the pathways traveled by students through Ontario post-secondary education (PSE). Through this study, we examine this topic by leveraging a large multifaceted linkage that brings together rich administrative data from the Toronto District School Board (Grades 9-12), Ontario college and university enrollment records (2009-2018), as well as government student loans and tax records. We use these data to statistically model differences in the PSE pathways traveled by more than 33,000 TDSB students. Our analyses identify statistically significant differences in the likelihood that students with/without disabilities will travel certain PSE pathways. However, such differences shrink drastically once we control for high school-level factors (e.g., academic performance, absenteeism). We elaborate on the importance of these findings for both social stratification researchers and policymakers.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".