HOW TO INCREASE MIGRANT RESILIENCE IN CANADA: WHAT THE LEGAL SYSTEM CAN DO TO HELP
Bibliographic record
Abstract
Immigration is important to Canada. Migrants replenish our declining population, help drive our economy, and contribute to our labour market. By 2036, almost half of the Canadian population will be either first-generation or second-generation immigrants. While the migrant experience varies, it is recognized to be deeply challenging. Among these barriers is often the need to engage with the complex legal system during and after the settlement process.\nThis paper uses the literature to better understand the benefits of migrants in Canada, concept of resilience, benefits of diversity in the workplace, and how law firms can leverage diversity to perform better. This paper demonstrates how law firms can benefit simultaneously while they help migrants foster higher levels of resilience by increasing their access to legal services. Research is used to provide both practical steps to take when incorporating diversity and metrics to consider when assessing the effectiveness of these efforts.\nCurrent news regarding corporate decisions are used to demonstrate the directions corporations and society are taking regarding their views on diversity. Recommendations are made to both the government and legal field to help address issues regarding access to legal 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".