Social Integration of Immigrants within the Linguistically Diverse Workplace: A Systematic Review
Bibliographic record
Abstract
As a consequence of international migration, a large number of workplaces are becoming linguistically diverse. This creates challenges for the workplace integration of immigrants and increases the risk of their social exclusion. A systematic review was conducted to determine the effects of linguistic diversity on social integration of immigrants within the workplace. Articles were identified by reviewing abstracts in electronic databases using key words related to linguistic diversity, social integration, immigrants and workplace. The search yielded ten peer reviewed articles, published in English, between 2000 and 2014. Information was extracted and synthesized from both quantitative and qualitative studies. The studies in this review were found to be concerned on three key areas of investigation: (1) social integration or social inclusion/exclusion, (2) social interaction and inter-group perception, and (3) accent discrimination. Smooth social and professional assimilation and the equality of opportunities for the immigrants were considered as the determining factors of their complete social integration in the workplace. Most studies in this review established a connection between social integration and local language skills; some also identified that factors such as racial discrimination and ethnicity based stereotyping contributed to social exclusion of immigrants, particularly when the studies involved visible minorities. The review enhances our understanding of challenges of complete social integration faced by immigrants and reinforces the need to invest in policies and program aimed at preventing marginalization of immigrants. However, due to limited number of studies identified by this review and the variation in findings, further research is necessary to investigate the role of linguistic diversity in the workplace integration of immigrants in immigrant receiving countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".