Le suivi par le SPIP du Rhône des jeunes majeurs condamnés pour des faits commis avec violence
Bibliographic record
Abstract
The Council of Europe Committee of Ministers has initiated European rules on probation (ERP) as a recommendation. In order to implement them, the French penitentiary national agency considered setting up a series of operational practice repositories (RPO). Thus, the probation service of the Rhône department (local authority) has developed a monitoring programme adapted to the specific features of young adults convicted for violent offences. The issue of this research aims to appraise how, based on these guidelines, probation monitoring of young violent offenders has some efficiency in their social rehabilitation process as defined by the ERP provisions and to what extent. The research programme was following several goals: • to pinpoint the specific personal features of these young violent offenders implying an adapted kind of monitoring, • to identify the tools, methods and practices used to monitor young adults, • to see if the specific monitoring implemented by the probation service meets the ERP recommendations for that matter, • to see if the specific monitoring for violent young offenders meets the general purposes of probation and rehabilitation. Methodology : the research team collected data with interviews of the miscellaneous players of this probation monitoring process. A sample of voluntary probation agents were interviewed as well as a small number of young probationers, Mediation focus groups using the so-called “photovoice” method were also carried out with the agents. Besides, statistical data were extracted from a one year sample of young offenders records from this probation service. Finally, a focus was placed on actuarial assessment devices, with a series of interviews among designers and analysts of these tools in Quebec. The results of the research appear in 6 chapters. They show that the young offenders may not be characterized by their offences, but rather with the relationship they have to the criminal institution and the understanding of their sentences. Hence, the agents’ main professional expertise lies with the building of a fruitful relationship with them. It implies that they understand the meaning of the probation measure and get involved in its requirements and the designed guidance by the agent. Thus the assessment of individual situations takes place in the course of this guidance period rather than during an initial time vetting reoffending risk and responsivity to guidance. Therefore, actuarial tools may enable some basic assessment; however, they do not appear as the main approach to fully appraise responsivity and capacities. The level of success of the probation process goes beyond the mere fulfilment of its formal requirements: it implies the probationer’s involvement in the measure enabling the triggering of a dynamic of desistance
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".