Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the labour market experiences of highly skilled migrants from developed countries who are not linguistic or visible minorities in the host country. Design/methodology/approach – The results of the paper derive from interviews with 64 highly skilled British migrants in Vancouver. Participants were asked open-end and closed-ended questions and the data from the interviews were coded and analysed manually. Findings – British migrants were divided with their labour market outcomes. Some cited positive experiences such as better responsibility, treatment and salary, while others cited negative experiences such as having to re-accredit, unduly proving themselves to their employers and not having their international experience recognised. Research limitations/implications – The results are particular to a single case study, hence they cannot be generalised or taken to represent the experiences of all British skilled migrants in Vancouver. Practical implications – Governments and organisations should ensure that they fulfil any promises they make to highly skilled migrants before the migration process and manage their expectations. Otherwise they face problems with brain waste and migrant retention in the short term and attracting foreign talent in the long term. They should also consider taking a more flexible approach to recognising foreign qualifications, skills and international experience. Originality/value – The paper adds to our understanding of migrant groups from countries who share similar social and cultural characteristics to the host population. The paper shows that labour market integration challenges are not exclusive to low skilled visible minority migrants, but also to highly skilled migrants who speak the same first language and have the same skin colour as the majority of the host population.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".