Integrating Better but Multilingually: Language Practices of South Asian Immigrants for Settlement and Integration in Canada
Bibliographic record
Abstract
Canadian Index for Measuring Integration (CIMI) is used by researchers, policy analysts, and government agencies to evaluate newcomer performance against the local-born population across four dimensions: economic, social, political, and health. Despite recognizing integration as a multidimensional and complex phenomenon, the index mainly evaluates the achievement of the four dimensions but without looking at how they are achieved (e.g., the role of different languages) and the type of integration (narrowed or broader) taking place. One underlying assumption can be that since Canada is a bilingual country, one of the official languages must be used for settlement and integration. However, as this study finds, this may not reflect the social reality of the Canadian society where diverse immigrant populations capitalize on official and non-official languages for settlement and integration. Utilizing the four dimensions with language as an additional variable, this quantitative study reports findings from 493 participants from a sub-group of South Asians from Bangladesh, India, and Pakistan who are able to settle and integrate better when English and ethnic languages are used for socio-politico-economic and health integration. In addition to reporting micro-level multilingual integration supported by ethnic concentrations, this study calls for further investigation of the type of integration in ethnic concentrations and its long-term implications for the Canadian society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".