Going against the grain: The role of skilled migrants' self‐regulation in finding quality employment
Bibliographic record
Abstract
Summary Skilled migrants constitute a significant and growing population in our knowledge economy, as they self‐initiate international careers in search of permanent resettlement. Yet, once in the new country, many skilled migrants face dire disappointments in that the jobs offered to them often fall short of their training and aspirations. The current quantitative study combines existing insights from qualitative migration research on the barriers to quality employment with self‐regulation research during job search. We identify mechanisms by which migrants may proactively self‐regulate towards better employment quality. Among 356 skilled migrants in Canada, migration‐specific barriers (a perceived lack of language proficiency and credential recognition) weakened migrants' self‐regulation efforts in the form of career‐related exploration (goal establishment) and planning (goal planning). Migrants' proactivity and social support received fostered self‐regulation, however. Career planning, albeit not career exploration, significantly predicted migrants' quality of employment 9 months later, particularly when combined with a high level of credential recognition. Results are relevant for the limited literature on skilled migrants' transition into the new labor market, the study of self‐regulation in challenging career contexts, and the prevention of unsatisfactory employment.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".