At the Black of the Class: Examining the Marginalization of Students of African and Caribbean Descent in Public Schools for Resolutions
Bibliographic record
Abstract
Black students experience hardships within the school system to a greater degree than many other students through the process of marginalization; the study explored this phenomenon to discover remedies. I conducted a literature review that found that Black students have higher dropout rates and are overrepresented in special education. Students are assessed through the disciplines of History and Social Studies which do not incorporate a meaningful Afrocentric voice. Organizations and schools with a majority Black population in North America were examined to discover the ways in which the subjugation could be eliminated. By conducting qualitative interviews, the study gained the perspectives of three educators aware of Black student oppressions. The study uncovered that Afrocentric education and celebrating Black student identities was of extreme importance and that the support of teachers and their awareness of intersectionality is fundamental. The implications are that Afrocentric education can be integrated in public schools and at the core are healthy teacher-parent relationships. Recommendations emerging from this study suggest that revisions to teacher preparatory programs and ministry policies, as well as the strategic recruitment of Black Male teachers will help support steps to change the current system.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".