Considering the roles of human capital and social capital in LINC Programming for newcomers in Canada
Bibliographic record
Abstract
Being able to speak English is one of the most important skills for newcomers who arrive in Canada without compentency in these language abilities. There are several formal services in Canada offered to newcomers for learning English, including Language Instruction for Newcomers to Canada (LINC), which is delivered by Immigration, Refugees and Citizenship Canada (IRCC). In addition to LINC, several community English-language programs have been developed in Toronto for different newcomer groups. Thus, to contribute to best practices for integrating newcomers, particularly refugees, into Canadian society through language training, the author will conduct an exploratory investigation of LINC and other language training programs, through the theoretical lenses of human capital and social capital. This dissertation will compile a list of actionable items that could be pursued to enhance the federal government’s LINC program and, ultimately, better serve the both the educational and community needs of newcomers to Canada. Keywords: human capital, social capital, LINC, ESL/EAL, language training, newcomer integration, settlement services
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".