A multicultural success story? Australian integration in comparative focus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it