“Why are we so Black?”: Nigerian families’ integration into schools in Canada
Bibliographic record
Abstract
There is a paucity of research on the education integration experiences of non-refugee Black African migrants. This sometimes leads to the essentialization of the Black African immigrant experience. In Canada, Nigerian immigrants are mostly economic migrants (not refugees), and due to the colonial history of Nigeria, are predominantly English speaking. Given that Nigerian immigrants in Canada account for a significant number of Black Africans in Canada, and that they are largely overlooked in the research, it is necessary to explore the experiences of Nigerian immigrant families and their integration into the school system. It is in this context that this phenomenological study sought to inquire into the education integration experiences of Nigerian parents and their children in an urban city in Canada. The purpose of the study was to inquire into parents’ experiences and their perceptions of their children’s experiences as they integrated into schools in Canada. The study was grounded in Critical Race Theory and Intersectionality frameworks and used journaling and interview techniques to explore these experiences. Findings illustrated the potential of play, friendships, parental involvement, and positive attitudes of educators in fostering integration and belonging. It also illustrates how race, microaggressions, and lack of connection with others impeded integration. Suggestions included that schools should explore the potential for play in fostering belonging for newly arrived children. Also, teacher training education institutions and K-12 schools should adopt anti-racist and anti-colonial practices and approaches to engage with stakeholders.
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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 | 0.002 | 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 | 0.038 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".