Bibliographic record
Abstract
The previous chapters have highlighted the complexity of integration and the range of facilitators that are needed to ensure it, for both children and adults. This chapter examines the integration of two national groups: Portuguese in the east of England and Sri Lankan Tamils in London. It reviews the evidence about the social and economic aspects of their integration and looks at how policy can better support those who are being left behind, who include super-mobile Portuguese workers who engage in circular migration strategies. A central argument of Moving Up and Getting On is that workplace experiences affect integration, and for some among the Portuguese and Sri Lankan Tamils, their present employment conditions have a negative impact on their future career progression and social lives. Integration policy, therefore, needs to consider migrants already in work and to engage with employers. Portugal, with a present population of 10 million, has a long history of migration. Between 1850 and 1974 over 2.6 million people left Portugal, for Brazil, Portugal’s African colonies, the USA, Canada and later France and Germany (Anderson and Higgs, 1976; Nunes, 2003). Despite attempts to control emigration, one million people left Portugal in the 1960s alone, the majority of whom came from rural central and northern Portugal. Today, Portugal remains one of the poorest countries in Europe and has suffered badly in the recent recession, a factor that has driven further migration. Until recently, the UK’s Portuguese community was small in comparison with those of France and Germany, comprising about 4,000 persons in 1975 (Barradas, 2005).
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".