Constructing Inequalities: Tenure Trajectories of Immigrant Workers and Union Strategies in the Milan Construction Sector
Bibliographic record
Abstract
Abstract In this mixed‐methods study, we examined the employment trajectories of immigrant employees in the construction sector in Milan and the role of unions in promoting their labour market inclusion. Drawing on a unique dataset of 417,004 contracts representing more than 166,000 construction workers over a 12‐year period (2000–2011), we employed Growth Curve Modelling (GCM) to explore national group differences in contract, firm and sector tenure trajectories. We found Egyptian and Romanian workers suffer from lowest tenure levels. To investigate these results, we conducted 15 interviews with key informants. Results suggested firm characteristics and position along the production process (mono‐task), pervasive immigrant hiring queues (mono‐national) and union's use of class strategies are interlocking forces that shape deep labour market segregation. We recommend unions develop and apply tailored ethnic strategies to empower highly segregated immigrant groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".