Bibliographic record
Abstract
Purpose Various forms of precarious employment create barriers to the integration and inclusion of migrant workers in receiving countries. The purpose of this paper is to review extant research in employment relations and management to identify key factors that contribute to migrant workers' precarious employment and highlight potential avenues for future research. Design/methodology/approach The authors conducted a narrative literature review drawing on 38 academic journal articles published between 2005 and 2020. Findings The authors’ review suggests that macro- and meso-level factors contribute to the precarious employment conditions of migrant workers. However, there is a limited articulation of successful practices and potential solutions to reduce migrant work precarity and exclusion. The literature on migrant workers' precarious employment experience is primarily focused on low-skilled sector (e.g. agriculture, hospitality, domestic care) jobs. In addition, few studies have explored the role of worker characteristics, such as gender, class, ethnicity, race and migration status, in shaping the experience of migrant workers in precarious employment. Practical implications The results of this research highlight the importance of engaging multilevel actors in addressing migrant employment precarity, including policymakers, employers and employment agencies. Originality/value This research contributes to a growing conversation of migrant employment precarity by highlighting the heterogeneity of migrant groups and calling for the use of intersectional lenses to understand migrant workers' experiences of precarious employment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".