Bibliographic record
Abstract
What it is to be a citizen is not a simple matter. For an individual to be a citizen is for that person to belong in a particular way to a community, be it a ‘city’ (as in the origins of the term), a nation state or some other broad grouping such as the European Union (EU). That an individual is a citizen of a community is a matter of law. However, the relationship also carries cultural connotations. Being a citizen implies that an individual shares certain beliefs with, and behaves as a member of, the community. The beginning of the twenty-first century has seen a number of nation states impose — or refine — tests to ensure that citizens to whom they grant the formal legal status have appropriate cultural attributes. Not only have the classical countries of immigration, such as Australia, Canada and the United States, strengthened or reintroduced stringent tests for migrants to become citizens, but the countries of Western Europe have, for the first time, also turned to testing regimes. Since the beginning of the century, the Netherlands and Germany have imposed tests of cultural knowledge for new citizens; the Netherlands has developed a civic integration regime which prospective migrants take before arrival; and the United Kingdom has revised its requirements of cultural knowledge and toughened its stance on visas and migration (Chapter 6). In a time of globalisation, it is remarkable that so many nations are insisting on nationally based cultural attributes for would-be citizens.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.083 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".