The Social Security Position of Irregular Migrant Workers: New Insights from National Social Security Law and International Law
Bibliographic record
Abstract
How should a State treat an irregular migrant worker, i.e. a foreign worker without authorisation to stay and/or work in the country, who is confronted with the realisation of a social risk? For instance, how should the State deal with an irregular migrant worker who becomes incapacitated for work due to a labour accident? Should he/she be treated like any other worker and qualify for income replacement benefits, medical benefits and labour-market reintegration measures? Or should the worker be denied benefits since he/she lacks work authorisation and may also lack authorisation to be in the country? This book addresses these questions and sets out proposals on how irregular migrant workers should be treated in national social security law. These are based on an analysis of the current social security position of irregular migrant workers and of a reference group, i.e. a country's own nationals who engage in undeclared work, in three selected Western States: Belgium, Canada and the Netherlands. In addition, the author conducts an in-depth investigation of the existing international legal framework (UN, ILO, EU, and CoE law), in order to ascertain the legal limits of any proposal on the legal position of irregular migrant workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".