Bibliographic record
Abstract
'The Racial Politics of Bodies, Nations and Knowledges' takes on the urgent task of chipping away at existing racial and ethnic hierarchies that obstruct global and local movement towards human rights and social justice. It imagines subjective, social and political spaces which might enable this movement. Many authors engage with Indigenous sovereignties, from both Indigenous and non-Indigenous perspectives. While most authors write from an Australian perspective, the issues addressed both have relevance beyond antipodean borders and complicate the idea of national boundaries. Chapters include a comparison of Indigenous struggles for land in Canada and Australia, the situation of minority ethnic and religious communities in the European Union, a meditation on teaching an Australian film about colonial history to German university students, and the story of the delicate positioning of a man of mixed Maori and Irish heritage finding cultural citizenship in the US academy in the mid 20th century. Other chapters focus on children's storybooks, media representations of suffering, and websites aimed at gay and lesbian youth - all international phenomena, and all places where racialised politics are at play. The book also offers insights into contemporary Australian racial politics via analyses of the treatment of asylum seekers, the health of Indigenous women, the education of young Indigenous people and the development of national histories in local tourist promotion.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".