Community Practitioners in Criminal Courts: Risk Logics and Multiply-Disadvantaged Individuals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The role community practitioners play in lower criminal courts has traditionally been overlooked in the socio-legal literature. In this article, I analyse how they use discretion and contribute to courts’ knowledge about and understanding of the problems clients (the accused) face. I focus on how they manoeuvre the courts, offering supports, service or housing; ‘vouching’ for or documenting progress, good character, compliance and potential; or educating judicial actors about social context, or therapeutic perspectives around mental health, substance use or trauma. I advance understandings of how non-justice actors interpret and use risk logics, while contributing medical, cultural and sociological knowledge that can improve practices in lower criminal courts, particularly by destabilizing assumptions about criminogenic risk management, poverty and choice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it