The Rule of Law and Access to the Courts for EU Migrants
Bibliographic record
Abstract
Abstract The ability of workers generally to enforce their labour rights in the UK has been a matter of ongoing discussion over a number of years. However, the dominance of the topic of immigration in the Brexit debates, along with questions surrounding the need for, and position of, EU migrant workers in the British labour market, has brought into sharp focus the issues facing the most vulnerable workers in their ability to enforce their employment rights. This, we argue, poses a serious challenge to the rule of law as defined by Bingham. For him, one of the principles of the rule of law is that access to courts is the ‘obvious corollary of the principle that everyone is bound by and entitled to the benefit of the law’. This leads to our consideration of, first, the perspective of formal enforcement (a ‘thin’ conception of the right of access to the courts) and second, effective substantive enforcement (a ‘thicker’ conception of the right). We argue that some of the reforms proposed in the Taylor review will be a thickened right of access for all workers, but a relatively thinner right for EU migrant workers, in which their particular vulnerabilities and obstacles are not recognized or ameliorated. The chasm between EU migrant and non‐migrant workers in the application of the rule of law would at the very least, remain, if not widen further.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".