Citizenship as an Instrument of Inclusion and Exclusion – A Comparative Analysis of Language Requirements in Naturalization Processes in the United States, Canada, Australia, and New Zealand
Bibliographic record
Abstract
Citizenship is an extremely complex concept and, as such, can be utilized in sociolinguistic research in order to account for differences in language policies. Language requirements in naturalization processes point to differences in particular countries’ language policies. Specifi cally, analysis of such language requirements reveals the different facets of the countries’ language policies in terms of the ways in which applicants for citizenship via naturalization are expected to know and use the countries’ offi cial language(s) or the main language (in cases where there is no offi cial language). The paper aims to address changes in such language requirements in four immigration countries which are bound by a specifi c past associated with colonialism and the fact that English is the medium of communication for the majority of the population. The countries are: the United States, Canada, Australia, and New Zealand. A comparison of past and current language requirements provides an insight into both past and current status of immigrants and their languages in the four countries. This, in turn, leads to assessments of citizenship increasingly being regarded more as an instrument of inclusion rather than as an instrument of exclusion.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".