Making Sense of Compulsory Schooling: Ontario Secondary School Graduation Requirements and Transitions to Postsecondary Education
Bibliographic record
Abstract
This thesis investigates the graduation requirements in Ontario’s publicly funded education system in Canada since the beginning of the twenty-first century, to shed light on the purposes of education and how education might contribute to human flourishing. The two-part study applies the capabilities approach, informed by critical race theory and the historical context of settler colonialism. The first part engages in a post-structural policy analysis of the secondary school graduation requirements to explore “What’s the problem represented to be?” in terms of the purposes of compulsory schooling. The analyses are based on over 300 Ontario policy texts from when the requirements were established by the Progressive Conservative government in 1999 through the Liberal government (2003 to spring 2018). In the present study, graduation requirements are used as proxies for the purposes of schooling as they define the policy context for the upper boundaries of compulsory school completion. Although individual actors within the education system have diverse purposes for the actions taken in the context of schooling, the policy context introduces inherent constraints. If the purposes are defined, it may empower actors within and outside the system to engage with compulsory schooling differently. The second part of the study examines the effects of the graduation requirement policies on students’ graduation and transitions to further education. The quantitative analyses focus on a cohort of students who began secondary school in 2013 in the Toronto District School Board (TDSB), which was the last cohort of grade 9 students to attend the 4-year secondary program during the previous Liberal minority government. Analysis of the associated effects of the graduation requirements on students’ graduation and transition to further education is essential for grounding the qualitative analysis of the requirements. In other words, how do the requirements relate to graduation and transition to further education? This study may contribute to the literature on transitions to postsecondary education through an examination of how policy may or may not contribute to changes in desired student outcomes. Ultimately this thesis sets out to explore why we have compulsory schooling, to inform how it might contribute to human flourishing.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".