An exploratory study on Toronto's immigrant youth's adaptation: A focus on social support
Bibliographic record
Abstract
This study focused on how social support, mainly peer and mentor friendships, affect the adaptational outcomes of immigrant youth in Toronto. Seventy-five students (50 female and 25 male) from Ryerson University, Humber College Institute of Technology and Advanced Learning and the University of Guelph-Humber who immigrated to Canada during adolescence responded anonymously to an on-line questionnaire. Questions focused on participants' ethnic identity, current level of self-acceptance, and current level of social support as well as the nature of supports and resources participants had upon arriving to Canada and when settling in. The purpose of this study was to assess what factors upon arrival to Canada and during adolescence have an effect on immigrants' social relationships and how these, in turn, may have influenced the self-acceptance of the participating immigrants to Canada. Findings indicate that self-acceptance was mostly related to constant positive support from family as well as current perceived social support from friends. Relationships with mentors, though helpful for many, did not have a significant relationship with self-acceptance. Theories on friendship development and the role mentors play in the adjustment process are also presented as well as recommendations for future research and program and policy implications.
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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 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".