Employer perspectives on workforce integration of self-initiated expatriates in Canada
Bibliographic record
Abstract
Purpose This study explores employer perspectives regarding barriers to and responsibility for the workforce integration of skilled immigrants. Specifically, this study assesses employer perceptions of how influential various barriers are to the integration of self-initiated expatriates (SIEs) in the workplace, uncovers employer perceptions of SIEs competence levels, identifies employer perceptions regarding multiple stakeholders’ levels of responsibility for SIEs integration and explores impactful means to overcome these barriers. Design/methodology/approach Given Canada’s dependence on SIEs for labour force growth, an online survey was conducted with hiring managers of 99 firms in a mid-sized city in Ontario, Canada. Findings The results demonstrate that employers shift the onus of responsibility for SIEs integration to other stakeholders (namely, the immigrant or government agencies), require documentation to evaluate human capital attainment of SIEs and may be systemically discriminating against SIEs. Research limitations/implications The results indicate a need for documented evidence to validate foreign education and skills previously acquired by SIEs. They advance research by providing a comparative assessment of barriers from the employer’s point of view. Practical implications The findings support the notion that employers should strategically partner with specialized private or government agencies to help with efforts to attract and evaluate SIEs. Originality/value Given that employers are key decision-makers regarding employment outcomes, this study investigates the underexplored role and perspective of employers in integrating SIEs. Additionally, this study provides both a holistic and a relative assessment of the barriers to and responsibility for SIEs integration, exploring the impact of each factor on employer decision-making.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".