Information sources and knowledge transfer to future migrants: A study of university students in India
Bibliographic record
Abstract
There is widespread recognition of the employment problems facing skilled immigrants in Canada. As a result, research reports high levels of frustration and anger over immigrants’ failed attempts to secure employment in Canada that is commensurate with their foreign-earned credentials and experience. Furthermore, research suggests that these employment problems are largely unanticipated by immigrants. As these employment difficulties have been observed for at least a decade, our study asks why immigrants are surprised by the difficulties they face in Canada. Our research questions focus on the sources of information being used by future migrants living in India. We wondered whether the information sources being used by future migrants are informing them of these employment struggles. To investigate, we surveyed 500 university students in India who plan to emigrate. Our findings confirm the pervasiveness of a reliance on informal migrant networks among future migrants in India, but also reveal how there is a discrepancy between the expected use of formal information sources, and the actual use of these sources. We conclude that the knowledge transfer of migration information is problematic, and we challenge the mainstream account of migrant social capital as a resource that minimizes the costs and risks of migration. Some policy suggestions are provided.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".