“It is Nice to Know that for Once Someone is not Just Saying that they're Backing your Corner, They are Actually Fucking Backing your Corner”
Bibliographic record
Abstract
Criminalised women remain one of the most marginalised voices with their experiences of community supervision largely absent from the evidence base underpinning practice. Research does, however, demonstrate the importance of gender responsive, trauma informed and service provision (Covington, S. (2007) ‘The Relational Theory of Women’s Psychological Development: Implications for the Criminal Justice System’. In Zaplin, R. (ed.) Female Offenders: Critical Perspectives and Effective Interventions , 2nd ed., Sudbury, MA: Jones and Bartlett Publishers; Hopper, E., Bassuk, E. and Olivet, J. (2010) ‘Shelter from the Storm: Trauma-Informed Care in Homelessness Services Settings’. The Open Health Services and Policy Journal , 3(2), pp. 80–100). In addition, the importance of supportive practitioner relationships, within a compassionate environment, that overcome shame and stigmatisation is reflected in wider literature. This is especially important as wider relational networks of criminalised women demonstrate limited strong and positive connections within environments of abuse, victimisation and dysfunction. This chapter will highlight the importance of criminalised women’s relational networks and how their experiences and behaviour should be considered within the context of trauma. Through narratives the qualities women deem important to enable them to develop strong, trusting, respectful and encouraging relationships during their community supervision will be presented. In addition, the safe and nurturing practice environments that create these relational opportunities and mutual peer support will be highlighted. Evidence will critique the limited continuity and flexibility within service delivery, concluding with practice recommendations that highlight the need for greater connections between policy, academia and practice.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".