Telling a different story : a counternarrative on Black graduates’ experiences at Canadian post-secondary institutions
Bibliographic record
Abstract
All too often, narratives in media, public discourse, and some scholarship in the United States and Canada focus on low access and retention rates of black students attending post-secondary institutions. Although it is important to understand the reasons tempering the success of students socialized as black, it is equally important to study how and why some black students succeed. The aim of this research inquiry, which was guided by a pragmatist approach, was to learn from the experiences of black graduates of Canadian post-secondary institutions, a topic that has received little scholarly attention in Canada to date. Specifically, this study examined how black graduates navigated obstacles, adopted success strategies, and drew on family and community support. This qualitative, phenomenological study engaged 16 participants from Ontario in a semi-structured telephone interview. An interdisciplinary theoretical framework engaging concepts of Critical Race Theory, Yosso’s (2005) Community Cultural Wealth Model, and Resiliency Theory was developed to support and form the interpretation of the findings of this study. Participants described everyday expressions of racism, faculty interactions and/or relationships, institutional resources, and academic challenges as presenting obstacles that created difficulties during their programs. The findings demonstrate that the participants employed a variety of resources and strategies, which led to their success. The results of the study allowed identification of specific success factors and strategies, such as encouraging and supportive faculty interactions and/or relationships, encouraging and supportive black faculty interactions and/or relationships, support groups, familial and kinship traditions and values, work ethic modelling, use of institutional resources, engagement of various forms of resistance tactics, and resilience. Findings explore the significance of various forms of community cultural capital, including aspirational, navigational, social, familial and resistant. Contextual differences such as participants’ personal and family histories and education experiences in Canada informs which of the community cultural capital were utilized.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.068 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".