The under-representation of Asian-Australians: political order and political delay
Bibliographic record
Abstract
The ideal of a liberal democratic political system is that its institutions should broadly reflect the make-up of the governed society.For a multicultural and migrant country such as Australia, this means that its elected legislatures should contain public representatives from a variety of backgrounds, commensurate to their numbers within the population.However, one noticeable feature of Australia's parliaments is the conspicuous lack of Asian-Australians.Here, Australia compares unfavourably to other multicultural parliamentary democracies such as Canada, New Zealand, and the United Kingdom.Although Asian-Australians have gained a presence in Australia's parliaments, they remain very under-represented.This poses the question as to whether Australia's political parties, legislatures, political cultures and norms have the ability to accommodate the country's changing demographics.At the most recent federal election in 2019 only three candidates with Asian ancestry were elected to the 151-seat House of Representatives, where government is formed.These three members of parliament make up just two percent of the chamber; while at the 2016 Census, people claiming full or partial Asian ancestry (East Asian, Southeast Asian, South Asian and Central Asian) comprised around 16 percent of the total Australian population.Of course, a perfect symmetry between population and representation may never be achieved given the limited number of
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".