Bibliographic record
Abstract
This research examines the experiences of males of African descent in higher education in Canada and explores the factors that motivate their commitment to their education, help them build resilience, and contribute to their success. Toward this end, I examined Black males’ educational achievement through the lens of an anti-oppression theoretical framework. This study employed qualitative methodology and interpretive phenomenological analysis, which enabled an in-depth exploration of the phenomenon. The research utilized semi-structured in-depth interviews as a data collection method. Purposeful and snowball sampling processes were employed for this research, and the participants were Black males currently succeeding in higher education or who had successfully attained at least an undergraduate degree. Samples were between the ages of 18–35. Numerous scholars have written extensively about the educational experiences of Black males in higher education. However, most studies are mostly from the United States. More research on this subject needs to be conducted within a Canadian context. Moreover, most scholars have focused on the underperformance of Black male students and their risk for failure. Other scholars have highlighted their resilience and success in post-secondary education. The findings of this study revealed that most participants gave credit to their families as a great source of motivation for their interest and effort in higher education but also found that they struggled with university in terms of learning, financial challenges, lack of support from faculty and staff, a lack of belonging, and selecting the right major. Despite these challenges, participants disclosed that dropping out was not an option.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".