Disconnect: An Examination of Black Students’ Educational Disengagement in Ontario School Boards
Bibliographic record
Abstract
This project is interested in investigating the impacts of educational disengagement on Black students’ educational experiences in Ontario school boards. I refer to exclusionary discipline as the removal of students from the classroom through disciplinary measures, such as suspensions and expulsions. Through exclusionary discipline, many students experience educational disengagement, which involves becoming withdrawn, detached, apathetic or uninterested in school or a more general lack of school belonging. This research aims to better understand students’ self-understandings about their experiences of educational disengagement, including the events leading up to disciplinary measures, their encounters with educators, and the impact of disciplinary measures on their interests in school.\nSignificant research in the Ontario educational context indicates that suspensions and expulsions are extremely detrimental to Black students’ educational outcomes. A large majority of these findings have been conducted through policy reviews or from the perspectives of authority figures such as principals, teachers and/or policymakers. While many studies have interacted with Black students directly, very few consider how they make sense of their experiences with educational disengagement. I employ Critical Race Theory to contextualize and analyze Black students’ experiences with educational disengagement.\nThrough qualitative interviews with three former students at various Ontario school boards, I present first-hand accounts of Black students’ experiences with educational disengagement. The data indicates that racial profiling continues to impact Black students’ educational outcomes as well as shape and constrain student educator interactions and relationships. Additionally, I provide evidence that experiences with disciplinary exclusion continue to negatively impact Black students’ engagement with education and learning. I also reveal that Black students feel they are unable to find the space to cope with oppression in their schools which forces them to shift from coping to finding strategies to survive. Finally, I provide a critical reframing of the concept of resilience to demonstrate the urgency needed to shift our focus on Black students’ ability to withstand hardship to the reasons why they experience them in the first place.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".