Co-producing Desistance Opportunities with Women in Prison: Reflections of a Sports Coach Developer
Bibliographic record
Abstract
The following paper provides a sport coach developer's refl ective narrative account of his fi rst experience of delivering a football-based development programme within a women's prison. The account highlights the notion that initial 'up-front' desistance work can be a process of co-production where all those involved engage in a journey of discovery in which the seeds of desistance are planted and begin to take root. The interplay between practitioners and service users involved navigating issues including vulnerability, trust and the impact of environmental factors, as well as highlights the idea that initial desistance eff orts result from coproduced eff orts between the person initiating change and those tasked with supporting this process. The paper calls for greater attention to the lived experience of facilitating early desistance transitions, as this will result in furthering our understanding of desistance processes.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".