Måling af behandlingsudbyttet af fængselsindsatser - en diskussion af GPPM
Bibliographic record
Abstract
In 2016, the Danish Prison and Probation Service introduced the Canadian tool GPPM (Generic Program Performance Measure for Correctional Programs) as part of MOVE, a cognitive-behavioural program for prisoners recently developed in Denmark.The tool measures the performance and progress of participants in correctional programs and allows the rating of offenders' skill development, motivation levels, attitude changes and program participation.Furthermore, GPPM output is used to give structured personal feedback to the individual participants on their performance during and after the completion of the program in order to support prisoners' developmental improvement.Using Danish correctional program data (LS/RNR) and GPPM results from 50 prisoners, we examine the different dimensions of the GPPM tool and discuss the possibilities of improving its applicability.First, we identify three separate dimensions of participant performance and argue that they provide a more detailed and accurate measure than the two dimensions identified by the original tool.Second, we find that the use of LS/RNR data in combination with GPPM data increases the likelihood of correctly identifying which prisoners will benefit from specific rehabilitation programmes thereby contributing to overall program effectiveness.Third, we note that an overemphasis on high-risk prisoners may distract from the fact that program participation for lowrisk prisoners can prevent an increase in their risk levels over time. BaggrundDer er de senere år produceret omfattende viden om vigtigheden af, at indsatte gennemfører evidensbaserede rehabiliteringsprogrammer, som kan reducere recidiv og derved beskytte offentlighedens sikkerhed (Wilson & Davis 2006; Nagin,
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".