A Comparison of High School Graduation Predictors Between Two Ontario Student Cohorts
Bibliographic record
Abstract
While a number of factors have already been shown to impact variations in graduation rates among students in Canada, there is little research examining the changing impact of these factors on Ontario students’ secondary education completion over time. This research draws on data from two Grade 9 cohorts (2006 and 2011) from the Toronto District School Board in order to unpack how predictors of high school graduation change over time. In particular, we use multivariate analysis to examine whether predictors (including gender, race, parental education, household income, suspension, academic achievement, special education needs, and Grade 9 absenteeism) are significant by cohort of students and if there are gaps in secondary school success between subgroups. Findings demonstrate that high school completion is increasing over time and that there is a diminishing importance of parental education and neighbourhood household income as a predictor of high school graduation. However, we do find evidence of persistent under-achievement among students of certain racial backgrounds, lower academic streams, and those with high rates of absenteeism. We argue that additional data infrastructure in Ontario and beyond are necessary to identify how our findings generalize to the province as a whole.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".