Empowering potential: Unveiling the realities of young Black males in Ontario
Bibliographic record
Abstract
This research investigates the experiences and effects of mentorship relationships on Black male youth aged 16-18, while also examining the social determinants of health that shape their future prospects. Employing a mixed-methods approach combining qualitative thematic analysis with quantitative statistical analysis, the study aims to enhance the generalizability and robustness of its findings. Data collection was facilitated through an online survey using Microsoft Forms, with 19 participants from Ontario, particularly the Peel region. The qualitative analysis revealed profound insights into the challenges faced by both mentors and mentees, including social stigma, discrimination barriers, and the development of emotional maturity among Black male youth. Quantitative data further confirmed the marginalization experienced by Black male youth, resulting in resource scarcity, low self-esteem, and compromised well-being, impacting academic achievements and mentorship dynamics. This research uniquely contributes to understanding the underlying factors affecting the self-efficacy and determination of Black male youth. It underscores the importance of institutional and governmental strategies to support their educational attainment and emphasizes the critical role of mentoring in their positive development. This study advocates for policies and practices aimed at addressing the systemic barriers hindering the advancement of Black male youth and ensuring that mentorship programs are effectively tailored to meet their specific needs.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".