Do institutions matter for refugee integration? a comparison of case worker integration strategies in Switzerland and Canada
Bibliographic record
Abstract
Abstract In this paper we explore the extent to which differences in institutional settings, with a focus on the human capital formation regime, shape the integration trajectories proposed to recently- arrived refugees. To do so, we compare two countries, Switzerland and Canada, which are committed to implementing integration policy for refugees and belong to two different human capital formation regimes. We investigate whether ending up in a country with a collective skill formation system (Switzerland) limits refugee integration paths by “managing” their aspirations and directing them towards predefined options compared to a country with a more liberal human capital formation regime (Canada) where refugees may have more room of manoeuvre to fulfil their aspirations. In order to test this hypothesis, we used qualitative vignettes and compared integration paths proposed by case workers to refugees in a Swiss Canton (Vaud) and in a Canadian Province (Québec). We found that overall, the integration paths proposed are very similar, regardless of the institutional context. We reason that this largely unexpected result is due to the similarities in the overall orientation of integration policy; the similarity of the policy problem and labour market shortage in the low skill segment experienced in both countries.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".