Thriving, catching up or falling behind: Immigrant and refugee children’s kindergarten competencies and later academic achievement
Bibliographic record
Abstract
Background with rationale Immigrant and refugee children and adolescents form a growing socially, culturally, and economically diverse group with varying adaptation outcomes. Main Aim In this Canadian, population-based study, we wanted to identify the varying academic achievement trajectories that immigrant and refugee children followed from childhood to adolescence (e.g., thriving, catching up, or falling behind) and whether these differences could be predicted at school entry, based upon select social/migration factors and teacher-assessed literacy, numeracy, and social-emotional competencies in kindergarten. Methods The study used a retrospective, longitudinal, population-based design and leveraged linked, individual-level administrative data from four sources (Immigration, Refugees, and Citizenship Canada, Ministry of Education, Ministry of Health, Human Early Learning Partnership) to identify a cohort of immigrant and refugee children in British Columbia, Canada (N = 9,216). We utilized a novel analytical approach (Group-based Trajectory Modeling) that allowed us to capture heterogeneity in the Grade 4 to Grade 10 academic (literacy and numeracy) trajectories. Results We found that immigrant and refugee children followed a range of academic achievement trajectories from Grade 4 to Grade 10 – some children thriving, some catching up, and others falling behind over time. A number of social/migration factors (e.g., sex and refugee status) as well as literacy, numeracy, and social-emotional competencies in kindergarten predicted these later academic trajectories in unique and sometimes powerful ways. Conclusion In all, we found that not all immigrant and refugee children start school on equal footing and this was associated with long-term outcomes. The implications for the importance of early, tailored interventions to set immigrant and refugee children onto paths of positive adaptation will be discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".