A multicultural success story? Australian integration in comparative focus
Bibliographic record
Abstract
Australia is often held up as an exemplary multicultural society in cross-national comparisons, particularly in relation to the integration of immigrants. Yet, this ‘grand narrative’ of Australia’s multicultural success risks an over-simplified picture of the dynamics of integration in Australia, obscuring dimensions on which Australia’s performance is comparatively poor. Juliet Pietsch’s Race, Ethnicity and the Participation Gap makes a valuable contribution to a more nuanced discussion, asking why the political participation of non-European ethnic and immigrant minorities in Australia is so low compared to Canada and the United States. This review article brings Pietsch into critical conversation with two recent books on comparative integration in North America and Western Europe: Richard Alba and Nancy Foner’s S trangers No More and Gulay Ugur Goksel’s Integration of Immigrants and the Theory of Recognition. Read alongside each other, these texts encourage deeper reflection on where Australia sits on a variety of indicators of immigrant integration as well as how integration is conceptualised in Australia. This article thus contributes to existing literature on the contemporary state of Australian multiculturalism, while also pointing towards directions for future research.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".