Telling the untold: First Nations people’s perceptions of policing in Broken Hill and Wilcannia
Bibliographic record
Abstract
This article examines the ways First Nations people perceive targeted policing practices in Broken Hill and Wilcannia, New South Wales. It focuses on First Nations viewpoints and perspectives to unveil the colonial legacies of the region, their infusion into contemporary policing practice, and the pressures those legacies exert on the community. The empirical material was generated through interviews with 19 First Nations community members, in collaboration with a First Nations advisory panel. By centring First Nations perceptions of policing, it contributes to critical colonial critiques and decolonising expertise of policing, focusing on subtle and perverse acts of policing in Broken Hill and Wilcannia, and how these are perceived by First Nations people on different structural, social and personal levels. The main argument is that instances of targeted policing are perpetrated against the collective community through methods of pressure and surveillance. This collective pressure specifically targets and alienates the First Nations community while maintaining a non-Indigenous social order. In doing so, police present a legitimate form of targeted policing that impacts community wellbeing in perverse and ongoing ways. This argument and approach enrich place-specific understandings of First Nations experiences of policing and unmask the dynamic aspects of policing that the community endures.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".