Naturalisation in decline? A cross-national study of recent immigrants in Australia and Canada
Bibliographic record
Abstract
This study investigates the paradoxical decline in citizenship acquisition in Australia and Canada, two nations historically defined by high immigrant naturalisation. Analysing census microdata from 2011, 2016, and 2021, we employ a comparative framework to assess trends while controlling for evolving immigrant sociodemographics. The results reveal a pronounced and parallel decline in naturalisation among recent immigrants (6–10 years since admission), a trend not explained by compositional shifts. Instead, the findings challenge monocausal explanations, demonstrating that the decline is driven by a complex interplay of factors: restrictive policy reforms that disproportionately curb uptake among lower-income and less-educated immigrants, and the ascendancy of transnationalism, which recalibrates the cost–benefit calculus for migrants from rapidly developing economies. The differential declines have led to an increased stratification in formal membership, diverging by admission category and origin country. The study concludes that the declining and increasingly stratified nature of naturalisation complicates its use as a straightforward metric of integration, underscoring a critical tension between global mobility and inclusive citizenship in the twenty-first century.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".