Regularisation in Europe and North America: Comparative Reflections on Societal Challenges and Benefits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent decades, irregular migration has emerged as a significant policy concern across the Europe and North America. While border enforcement continues to dominate political discourse, regularisation — mainly the conferral of a regular residence status to those without it — has also become a key policy instrument. Indeed, States have intermittently adopted mass amnesties or individualised mechanisms to address the legal and social limbo of irregular residents. Even in Latin America, over 100 regularisations have been implemented in the 21st century. Such measures are part of the toolkit for policymakers dealing with irregular migration, although they cannot invariably be regarded as standard programmes. This is evident not only in contexts characterised, on the one hand, by high numbers of irregular migrants and, on the other, by persistent labour demands which create ambivalent opportunities. The rationale for regularisation is often but not always pragmatic: persistent labour demands, humanitarian concerns, and the presence of long-term undocumented residents contribute to adopt and shape regularisation solutions. This report undertakes a detailed comparative analysis across nine Global North countries: Canada, Ireland, the United States, Spain, Germany, Portugal, Belgium, France, and Italy. Drawing on country-specific research and interview evidence from these cases, the objective is to analyse the societal challenges and benefits of regularisation. The report also aims to highlight how the selective and conditional logic underpinning regularisation schemes may reproduce social hierarchies. It contributes to current debates by addressing regularisation capacity to either challenge or reaffirm civic stratification and by reflecting on its broader implications for labour markets, welfare systems, and public goods. Importantly, we underscore from the outset that our objective is to examine the limitations and contradictions of regularisation policies, while maintaining a forward-looking and constructive perspective. Although often selective, fragmented, and embedded within broader exclusionary frameworks, regularisation schemes nonetheless represent meaningful—if constrained—opportunities. Migrants are not merely passive recipients of these measures; rather, they often mobilise them strategically, navigating their complexities in ways that may elude or surpass established critical categories. By foregrounding these tensions, this report seeks to advance a nuanced understanding of regularisation as a site of both constraint and possibility, with the ultimate goal of informing more inclusive, just, and effective migration governance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it