Bibliographic record
Abstract
Purpose This paper seeks to address two research questions: first, to what extent do highly skilled migrants intend to make personal business and financial investments in their home countries, and second, what factors influence them to invest in their home countries? Design/methodology/approach The results are based on face‐to‐face and telephone interviews which took place between September, 2008 and March, 2009 with 64 highly skilled British migrants working in Vancouver, Canada. Respondents were asked a combination of open‐ and closed‐ended questions. Findings The results of this study find that the vast majority of respondents are not investing in or intending to return to their home country, which indicates that they contributing to brain circulation in a limited extent. Practical implications The paper argues that governments and organisations in the home country can play an important role in facilitating brain circulation in Europe. Originality/value Much of the academic literature suggests that the brain drain has now transformed into brain gain. The findings of this study do not support this shift because most of the sample of British expatriates in Vancouver are not intending to invest in or return to Europe. This is significant because highly skilled migrants could be better utilised as resources by European governments and organisations.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".