Nonwhite origins, Anglo destinations: immigrants in the USA and Britain
Bibliographic record
Abstract
Until very late in the twentieth century, the USA was the setting for most statistical studies of ethnic inequality; Canada ranked second, Australia a distant third. This situation reflected the high proportions of immigrants in these countries and the large amount of information that researchers could obtain about the foreign born. After World War II, however, the numbers immigrating to Europe began to grow. Today, annual immigration to Europe is twice as high as annual immigration to the “New World” (Widgren 1994). As a result, Europe's immigrants have attracted increasing amounts of research attention. Indeed, several European nations now field surveys explicitly designed to illuminate the experiences of their ethnic minorities. Studies of Britain's ethnic minorities stand at the forefront of this new scholarship. The first survey specifically devoted to this population was launched in 1966; more exhaustive studies followed in 1974, 1982, and 1994. To be sure, in the early years, the data collected in these surveys were available only to a small group of scholars. But today researchers can obtain the responses to the 1994 National Survey of Ethnic Minorities on CD-ROM from the Data Archive at Essex University. Of course, already in the late 1980s, the British Labour Force Survey was available on computer tape; by 1993 the UK Census was accessible on the University of Manchester's mainframe. And each year new sources of information on Britain's immigrants and minorities become available.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".