Precarity, Opportunity, and Adaptation: Recently Arrived Immigrant and Refugee Experiences Navigating the Canadian Labour Market
Bibliographic record
Abstract
Canada relies on immigrants as key drivers of the country’s population and economic growth. Composing a quarter of the Canadian labour market, immigrants accounted for over 80 per cent of population growth between 2017 and 2018 (Yssaad & Fields, 2018; IRCC, 2020). Recognising this benefit, the federal government has projected increasing admission levels by 1% of the population and reach 451,000 new permanent immigrants per year by 2024 (IRCC, 2022a). Yet beyond the numbers, and despite clear advantages of a smooth transition into the Canadian labour force, many migrants and refugees experience a multitude of barriers that impede their earnings and pathways to sustainable livelihoods. Research has examined various dimensions such as skill devaluing (Bauder, 2003; Creese & Wiebe, 2012), the effects of neoliberal restructuring (Bhuyan et al., 2017; Hiebert, 2006), bias and discrimination in hiring practices (Esses et al., 2007; Fuller & Martin, 2012), the role of points-based selection policies (Sweetman & Warman, 2013; Warman et al., 2015), and the impact of familial structures (Dyson et al., 2019; Shields & Lujan, 2019). Furthermore, the Covid-19 pandemic has introduced new dimensions that interplay with existing labour market barriers and enablers facing newly-arrived immigrants and refugees. In particular, job loss as a result of the pandemic was more significant for recently-arrived immigrants, who saw a reduced employment rate during the initial months of the pandemic compared to Canadianborn workers (Cornelissen & Turcotte, 2020).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".