Myths and counterarguments: a quick reference summary
Bibliographic record
Abstract
Immigration The evidence: The UK experience of international migration is not remarkable when set in a global context The myth: Britain has an unfair share of immigrants • The number of immigrants in Britain (foreign-born) increased from 2.6 million in 1961 to 5.4 million in 2005. This rise of 110% is the same as the worldwide increase. These UN calculations take into account the changes in boundaries in Europe and the Soviet Union (pp 55-56). • Increased international migration is a common experience for developed, economically strong nations. Immigration is expected for countries with strong economies as international moves are shaped by patterns of supply and demand of jobs and labour (pp 59, 84-85). • Not only has the UK’s immigration grown in line with world migration, but the UK has a smaller proportion of immigrants and lower rates of net immigration than the US, Canada, Australia and several large European countries (pp 55-56, 59-60, Table 3.2). • Less than 3% of the world’s migrants live in the UK compared with 5% in Germany and 20% in the US (pp 59-60). • Migrants (those born outside the country) make up 9% of the population in the UK compared with 12% in Germany and 13% in the US. 9% is the average for Europe (p 60, Table 3.2). • The UK’s net in-migration rate is 2 per 1,000 population compared with 3 in Germany and 4 in the US (p 60, Table 3.2). For more on Britain’s immigration experience in global context, see Chapter Three. The evidence: Measurement of international migration requires care, and recognition of the diversity of migrants The myth: We all know how much immigration there is (too much) • The challenges of measuring international migration do not justify an assumption that levels of immigration are problematically large (pp 54-56). • Ethnicity and immigration should not be confused: half of all minority residents were born in the UK and two thirds of immigrants are White (p 57). • Undocumented migration is by its nature not measurable (and can only be estimated), except after an amnesty (p 57). For more on measuring immigration, see Chapter Three. The evidence: Immigrants are diverse and increasingly short-term stayers The myth: Britain’s flooded with problem immigrants • Immigration to Britain in the year prior to the last census was equivalent to less than 1% of the population.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.047 | 0.017 |
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".