Using an intersectional lifecourse approach to understand the migration of the highly skilled
Bibliographic record
Abstract
This chapter combines an intersectional approach with a lifecourse perspective in order to understand the migration of the highly skilled. An intersectional lifecourse approach contextualizes migration processes, considers the role of both structure and agency, and the macro and micro factors underlying peoples’ moves. These theoretical ambitions are best furthered, the chapter argues, in conjunction with a methodology that draws on multiple types of data sources. In order to illustrate how such a project might be carried out, the chapter draws on a study conducted with people of Iranian origin who first moved to Sweden, and then moved again to London or Toronto. Findings suggest that for these highly mobile people, deciding to make multiple migrations was based on a variety of factors. While employment opportunities were an important aspect of the decision making process so too, were a number of non-economic factors. Overall, the chapter critically identifies, evaluates and aims to address some of the challenges that often arise in migration research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".