Government-assisted Refugees in Toronto's LINC Classes: An Exploration of Perceived Needs an Barriers
Bibliographic record
Abstract
Studies on any aspect of the resettlement of government-assisted refugees (GARs) in Canada are scarce. This lack of research is particularly prominent in the area of GARs' experience in official language-training programs. Drawing on both quantitative and qualitataive data, this paper is the first examination of the perceived needs and barriers of GARs in Language and Instruction for Newcomers to Canada (LINC), a federally-funded langauge training program for newly arrived permanent residents. The study focuses on the LINC program in the City of Toronto. Analysis of quantitative data suggests that GARs have high drop-out and low graduation rates from LINC classes compared to other immigrants. Interviews with key informants parallel the findings from the quantitative data, but also identify significant difficulties faced by GARs both inside and outside the LINC classroom. This study contributes to an enhanced understanding of the settlement needs of GARs and advocates for the development of both new and improved programs and services for GARs in Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".