Cultural Capital during Migration—A Multi-level Approach for the Empirical Analysis of the Labor Market Integration of Highly Skilled Migrants
Bibliographic record
Abstract
The integration of highly qualified migrants into the labor market can be an opportunity for knowledge societies because their prosperity depends on the incorporation and improvement of cultural capital. In this paper we present a qualitative research approach with which we analyze on several levels how migrants make use of their cultural capital during their entry into the labor market: in addition to the biographical experience of migrants we analyze how this experience is embedded in milieus, social networks and self-organizations (meso-level) and structured by the macro-level of judicial regulations of immigration and labor market policies. Our empirical analysis is focused by the assumed importance of educational qualification and residence status during entry into the labor market. Four different groups of empirical cases, which differ with respect to the level of education, the place of its acquisition (at home or abroad) as well as to their residence status, are compared to each other. In order to study the contingencies of meso and macro-social contexts, labor-market integration will be examined in the context of Germany as well as in Canada, Great Britain and Turkey. URN: urn:nbn:de:0114-fqs0603143
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".