Does Diversity and Inclusion Include Immigrants? Employer and Skilled Newcomer Perspectives
Bibliographic record
Abstract
In this exploratory study, we examine the role that employers play in skilled immigrants’ employment integration by bringing together the experiences of newcomers with the insights of Canadian employers. Using a grounded theory approach, we analyze qualitative interview and focus group data from 48 newly arrived skilled immigrants and 41 employers to highlight the ways human resource management policies and practices shape the employment experiences of skilled immigrants. Our results indicate that while employers claim to value diversity and inclusion (D&I), most do not have coordinated policies or practices in place to promote equity for disadvantaged job seekers or employees. Even the few employers with D&I initiatives in place do not explicitly include immigrants as a group warranting special accommodation. For the most part, employers have a strong preference for Canadian education, experience, and workplace behaviour. From our results, we offer recommendations for organizations to better leverage the talents and contributions of skilled immigrant workers, critique the narrow lens through which immigrant employment is examined, and point to the need for more interdisciplinary research on immigrant employment outcomes to incorporate the voices of all stakeholders.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".