The Darker Side of the Internal Market Ideal: EU Migrant Workers Living in a Coastal Town
Bibliographic record
Abstract
Abstract The free movement of people is one of the cornerstones of the internal market, allowing any EU citizen to live and work in another Member State (MS). Much research has been done on intra-Member State migration to metropolitan cities, research which highlights the benefits of free movement. What is less understood is the impact of free movement on small, regional locations which previously experienced little to no inward migration. Our research aims to start filling that gap. It examines the experiences of EU migrant workers who moved to the UK (pre-Brexit) exercising their EU freedom of movement rights. In particular, it looks at EU migrant workers working in low-wage, low-skilled employment in the coastal town of Great Yarmouth in Norfolk, in the East of England. We look specifically at the problems EU migrant workers experience in and around housing, an issue which had already been identified by the Member States in the 1950s as a potential flashpoint. Housing has always been a sensitive issue for receiving states, as well as for locals and for migrants. We wish to argue that the requirement of equal treatment, the key right under EU law, means little in practice for the day-to-dayexperience of migrant workers yet it creates tensions within the local community.
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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 | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".