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 preand 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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".