Bibliographic record
Abstract
The research clearly indicates that the vast majority of individuals involved in the justice system who display offending behaviour have experienced trauma, victimization, or Adverse Childhood Experiences (ACEs). Knowing this to be empirically factual raises the question, why is this not highlighted in the training of police officers, correctional officers, parole and probation officers, crown prosecutors, defence lawyers, and judges alike? An understanding of the Justice Client and their complex trauma could have important consequences on how all justice actors interact with people who experience the justice system. Knowing that these individuals were often victims long before they were offending could bring a more compassionate lens to the justice system. Having traumatic experiences is not the cause of offending, but it is often present in the offending population. The prevalence of trauma among the offending population, who themselves have often traumatized their victims, suggests a much-needed change in how police are trained to interact with Justice Clients. This paper applies the concept of Universal Precautions from first aid training in the development of practical policy to create a justice system based in compassion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.071 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".