Imperiled Multiculturalism? COVID-19, Racism and Nation-Building in Australia
Bibliographic record
Abstract
Australia’s multiculturalism, frequently lauded as a success, has been seriously wounded by the COVID-19 pandemic. Alongside increased racism directed at people from Chinese and Asian backgrounds, there has been a closure of Australia’s borders, not only to the rest of the world but also between its states and territories. These developments, combined with a recent surge of populist nationalism, signal a shift away from an open, globalized Australia to a more closed Australia—anxious and fearful of external threats. Whether Australia’s multiculturalism can emerge from the pandemic intact, rather than imperiled, demands on there being an empathic renewal of political support for multiculturalism and anti-racism. This chapter explores the historical development of multiculturalism in Australia (and its parallels with Canada), and how recent events illustrate a lapse in Australia’s nation-building imagination.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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 teacher head, 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".