Bibliographic record
Abstract
Many videos went viral after the military coup in Myanmar in February 2021, however, one that was particularly disturbing were the images of a group of police publicly beating a young man who had a physical and cognitive disability. There is an absence of systematic evidence internationally on the extent of police violence, including lethal violence, against people with disabilities and mental ill-health – this absence is part of the state’s strategic ignorance which disavows knowledge of the targeted victims of state violence noted in Chapter 5. However, we know from individual cases and research data that the problem is extensive. Some of the most well-known police killings in the US which spurred the BLM movement involved Black Americans with disabilities, including Eric Garner, Tamir Rice, Tanesha Anderson, Freddie Gray, and Sandra Bland. In Canada in the 3 months between April and June 2020 in the lead-up to mass protests over police killings, six people died during mental health-related contact with police: Ejaz Ahmed Choudry, Rodney Levi, Chantal Moore, Regis Korchinski-Paquet, Caleb Tubila Njoko, and D’Andre Campbell. All were Black, Indigenous, or people of colour. Four were shot dead by police. The other two fell from balconies after police intervened. The circumstances of these deaths provided a powerful impetus to Canadian calls to defund the police and to provide non-punitive support for people in need. Similarly, in Australia, police fatal shootings and violence against people with disability have been detailed in evidence to recent state and federal royal commissions into mental illness and disability. It has also been a source of First Nations activism in relation to deaths in custody. According to The Guardian ’s database Deaths Inside, during 2018–2019, nine First Nations people with disability died in police custody. There is extensive police intervention into the lives of people with mental ill-health and cognitive impairments, and policing is a key part of the disablist and ableist processes of state control. I use the term dis/abling to encompass these dual processes of (i) the directly disabling effects and outcomes of police violence and trauma which cause disability, and (ii) police intervention, criminalisation of, and violence against people with disability because of ableist assumptions of what constitutes normative behaviour.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".