The Level of Service Inventory-Revised Profile of English Prisoners
Bibliographic record
Abstract
This study considers the applicability of the Level of Service Inventory-Revised (LSI-R) with an English prison population. After slight modification of the LSI-R for use in England, several items were added to amplify it for use in prisons. As data from an English prison population have not previously been published, full details are presented. Comparison with data from a Canadian prison population suggests that the LSI-R functions in a similar manner in assessing needs for both populations. The calculation of test-retest change scores over the duration of the sentence, based on the dynamic risk items, represents a new use of the LSI-R. This study precedes another study presently under way using this data set to search for relationships between LSI-R scores and recidivism. Such relationships, if reliably established, would have several applications within the prison service in terms of sentence planning and risk assessment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".