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Record W3133385953 · doi:10.37839/mar2652-550x5.17

The under-representation of Asian-Australians: political order and political delay

2020· article· en· W3133385953 on OpenAlexaboutno aff
Grant Wyeth

Bibliographic record

VenueMelbourne Asia Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCommonwealth, Australian Politics and Federalism
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsRepresentation (politics)Order (exchange)Political scienceEconomicsLaw

Abstract

fetched live from OpenAlex

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

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.398
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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