Race, Ethnicity, and the Participation Gap: Understanding Australia's Political Complexion
Bibliographic record
Abstract
Race, Ethnicity, and the Participation Gap begins with the argument that political institutions in settler and culturally diverse societies such as Australia, the United States, and Canada should mirror their culturally diverse populations. Compared to the United States and Canada, however, Australia has very low rates of immigrant and ethnic minority political representation in the Commonwealth Parliament, particularly in the House of Representatives. The overall existence of racial hierarchies within formal political institutions represents an inconsistency with the democratic ideals of representation and accountability in pluralist societies. \n \nDrawing on findings from the United States, Canada, and Australia, Juliet Pietsch reveals that the lack of political representation in Australia is significant when compared to the United States and Canada, revealing a serious democratic deficit. Her book is devoted to exploring this central puzzle: why is it that, despite having a similar history to other settler countries, Australia shows such comparatively low rates of political participation among its immigrant and ethnic minority populations from non-British and European backgrounds? In addressing this crucial question, Race, Ethnicity, and the Participation Gap examines the impact of Australia’s alternative path on the political representation of immigrants and ethnic minorities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".