Service Providers' Perspectives on the Pathways of Adjustment for Newcomer Children and Youth in Canada
Bibliographic record
Abstract
Over the past several decades, the demographic population of Canada has significantly transformed.Most striking is the influx of recent immigrant families into Canada, which currently hosts the second highest population of immigrants and refugees in the world.Almost one of every five Canadians is an immigrant, with 36% (390,800) representing immigrant and refugee children and youth 24 years of age or under.It has been estimated that by 2017, visible ethnic minorities will account for up to 23% of Canada's population (Statistics Canada, 2006).As the young population lead the way for a "new" Canada, it is imperative for researchers, service providers, and social policymakers to investigate and overcome the multiple challenges and barriers that newcomer children and youth face as they navigate through their adjustment and settlement pathways.As children and youth recreate their lives in a new country, they undergo an acculturation process that entails them to adjust behaviorally, psychologically, and socially into the mainstream society (see Berry and Sabetier, in volume).Although the migratory process is bounded by the complexities of pre-and post-settlement and adjustment factors, there are some shared challenges and barriers.First, the experience of migration leads to significant life changes to one's physical and sociocultural environments as well as interpersonal relationships (Anisef, 2005).Many newcomers will struggle with the official language of the host country.For example, in 2001, 46% of all immigrants reported that they could not speak either English or French.Those under 15 years of age were the least likely to 08_Chuang11_C08_p149-170.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.045 | 0.011 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".