Tackling student disengagement: examining the perspectives and philosophies of black educators in a canadian context
Bibliographic record
Abstract
Disengagement amongst Black students is a pressing matter as recent studies suggest a disproportionate number of dropouts amongst Black students compared to their non-Black counterparts. This study begins with an analysis of multiculturalism in Canada, which provides the foundation for the discussion of race and equity in a Canadian context. The unjust inherent nature of multiculturalism fuels the systemic racism and inequality prevalent and institutionalized in society, through the education system. The myriad issues related to race, power, privilege and class inform the current situation in our schools – Black students are disengaged and as a result are dropping out of school at alarming rates. This study will focus on the issue of disengagement and the possible causes leading Black students to leave school prematurely, through the eyes of Black educators.Through a qualitative research design I conducted a series of phenomenological interviews with Black educators within the Toronto and Peel District School Boards in order to ascertain their philosophies and perspectives as they pertain to disengagement in schools amongst Black students. Through a culturally relevant pedagogy, critical pedagogy and anti-racist framework I engage in an interview process that captures the voice of Black educators on what they see as the causes for disengagement. The final chapter of this study suggests that mentorship, care, low expectations for students, culturally relevant teaching and curriculum, and diversity amongst staff are significant factors impacting disengagement. The analysis of these themes suggests ways of closing the gaps in the education system so that Black students can reengage in schooling.
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 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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.078 | 0.038 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.012 |
| 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".