Prison blues and token truths: Inside the reality and fantasy of first nations representations in Australian women's prison drama Wentworth
Bibliographic record
Abstract
The Australian TV dramas Prisoner (1979- 86) and Wentworth (2013- 21) have been produced in a culture of continuing White colonization and domination. This chapter analyses the characters and representations in Wentworth in the context of the overrepresentation of incarcerated First Nations women in Australian prison systems, and also looks back to how First Nations women were represented in Prisoner. By using theories by and about First Nations people to guide the process of analysing and interpreting these two Australian series, this chapter highlights the common absence of and tropes associated with First Nations characters, addressing the key issues of silences and stereotyping. Guided in particular by Indigenous theories of epistemology, critical race studies and standpoint theory, this chapter explores how representations of First Nations characters on Prisoner and Wentworth contribute to the inequality of First Nations women in Australian society and in particular the challenges of those experiencing incarceration. This chapter concludes that, despite the landmark recognition of the violence and widespread impacts of incarceration on First Nations people by the Royal Commission into Aboriginal Deaths in Custody from 1987, there has not yet been a major storyline addressing this problem in over 700 episodes of Prisoner and Wentworth.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".