Black youth disengaging from Ontario’s educational system : Grounded theory of their educational experiences
Bibliographic record
Abstract
[Introduction]: “Canadian school systems and institutions are faced with substantial issues of diversity, as there is a growing conscious realization that these systems and institutions are pervasive with issues of anti-Black racism (Dei, 1997; Ilmi, 2011; Sibblis, 2014; United Nations [UN] Working Group, 2016). Since the 1960s, there have been growing concerns about the many challenges experienced by Black and other minority students in schools in Ontario, Canada (Dei, 2008; James & Turner, 2017; Ruck & Wortley, 2002). For example, racialized students have been labelled with learning disabilities, streamlined into primary and general classes, and shown to experience high levels of disengagement and dropout (James & Turner, 2017; Dei, 2008; McMurtry & Curling, 2008; Anti-Racism Directorate, 2017). Presently, school engagement for young Black people remains a concern (UN Working Group, 2016). This chapter explores the experiences of Black Caribbean students navigating the Ontario educational sector. Our research aims to understand various forms of disciplinary action used against Black young people and how this influences their educational engagement and perceptions.”
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".