Irregular Migration and Irregular Work: A Chicken and Egg Dilemma
Bibliographic record
Abstract
Abstract The connection between irregular or undeclared work and irregular migration often combines in an explosive mix that stirs anxieties about the state’s control over migration flows, labour market regulation, and unfair competition with native workers as well as lost revenue for the state. This chapter discusses the different types of irregular employment and irregular migration and the intersection between the two to construct a typology of irregular employment of irregular foreign residents. We then investigate the dynamics of specific labour market sectors where irregularities in employment thrive, notably in domestic and care work, agriculture and the construction sector. The chapter adopts a double comparative European perspective, surveying findings from different countries, notably the Nordic states, the UK, the Netherlands, Germany, and Italy with a focus on the different sectors. We seek to explain the role of (irregular) migrant workers within each sector and analyse the related socio-economic and policy dynamics. The chapter concludes with a reflection on the role of enforcement and sanctions vs a variable geometry of regulation for specific labour market sectors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".