Bibliographic record
Abstract
The intake of immigrants and refugees by countries such as Canada, the US, the UK, and Australia has significant implications for involved adults and children. As they navigate their heritage nations, the acquisition of the English language becomes essential for education and vocational purposes. In essence, to be integrated into their heritage nations, migrant adults and children face the challenge of having to learn a second language. It is important to acknowledge the disruption of education and careers experienced by these individuals as they seek new beginnings in heritage nations. While heritage nations such as Canada have programs that facilitate the integration of immigrants and refugees, some of the initiatives fail to take into account their diverse needs. For example, Fang et al. (2018) indicate that refugees face gendered barriers, low education levels, emotional scars and physical impairments, and cultural barriers, which hinder their acquisition of the English language. Understanding the specific challenges immigrants and refugees face as they transition to their new environment is critical to improving their experience. This paper examines the psychological, socio-cultural, and educational implications of education and career disruptions of immigrants and refugees and suggests strategies to support new language acquisition. Received: 30 September 2024 / Accepted: 2 November 2024 / Published: 20 November 2024
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".