Exploring the Factors That African Refugee-Background Students Identify as Being Helpful to Their Academic Success
Bibliographic record
Abstract
African refugee-background (ARB) students achieve high standards of success, yet their lived experiences are frequently absent from educational literature in Canada. Current and past research has focused on their academic deficits, their vulnerabilities, and their maladjusted behaviour, neglecting the positive attributes they bring to their host countries. Using specific data collected from semi-structured interviews with eight male and female ARB high school graduates between the ages of 18-25, this qualitative study employed a critical race paradigm to explore factors that ARB high school graduates identified as being helpful in their academic success. The study sought to challenge the deficit views on ARB students’ education by highlighting the perspectives of academically successful ARB students in a secondary school setting. The findings from the ARB students’ narratives highlighted three major themes: (a) success extends beyond the classroom and it cannot be normalized, (b) success is multifaceted and attainable by all, and (c) intrinsic motivation and resilience is a coping strategy for academic success. Additionally, the findings indicated that ARB students used a variety of coping strategies to overcome the negative and stressful environments in their high schools. Disseminating their narratives of success provides real-life examples for other refugee-background students to emulate, in pursuit of their own academic success, amidst the educational and societal barriers that they encounter. These findings add to the limited amount of research on ARB students’ academic success and may provide alternative strategies on refugee education for
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".