Bibliographic record
Abstract
Trauma-informed care and practice was developed over 20 years ago and is emerging as a way of working for the police, in corrections and courts, and broader contexts such as health and education services. I started my criminal justice career with South Australia Police in 2001. It was not until 2014 that I first heard the term “trauma-informed,” which was regarding victim services. Back then, it was unclear how it might be applied in practice. Soon after, I was appointed to the Parole Board of South Australia in 2015. As a Parole Board member, I have interviewed more than 1,000 adults, many with chronic offending histories and most with trauma histories. In this paper, I draw from local case studies and apply the SAMHSA (2014) trauma-informed practice framework to explore “trauma-informed” policing. Trauma is understood to mean the impact of adversity (i.e., potentially traumatic events and experiences) on an individual’s functioning and well-being. I outline the ABCs of a trauma-informed policing response: (A) trauma-informed policing requires an agenda, (B) trauma-informed policing is broad, and (C) trauma-informed policing is compassionate. Without the ABCs of traumainformed policing, police responses may be, at best, well-intentioned good practice, but they should not be considered to be trauma-informed.
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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 | 0.048 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.020 | 0.173 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".