A multi‐level process perspective on refugee workplace integration
Bibliographic record
Abstract
Abstract Prior research has suggested the need for complementary efforts by multiple stakeholders (government, NGOs, employers) to support workplace integration for refugees, but has paid less attention to how such programs are developed and enacted in specific settings, and how they then influence refugees' workplace integration trajectories. In this article, we develop a process perspective on refugee workplace integration, drawing on a qualitative longitudinal study of the deployment of an internship and mentoring program for skilled refugees in a Swedish manufacturing firm with a history of engagement with diversity and inclusion issues. The data include documents, observations, and 32 interviews with managers, mentors and refugee‐newcomers, the latter interviewed at three points in time during, shortly after, and three or more years after the internship. We derive a process model showing that although practices at the company level may be developed and enacted in a way that reflect goal complementarity between stakeholders, refugee experiences are nevertheless permeated by a shifting pattern of dialectic tensions as refugees progress through four phases of interaction with the firm (pre‐internship, internship, transition to employment, post‐transition). We highlight the central mediating role of the inclusionary practices of front‐line managers and mentors' in interaction with refugee‐newcomers' agency, learning and adaptation in facilitating transitions to first employment and even beyond.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".