Development and Validation of a Parole Quality Assurance Inventory (PQAI)
Bibliographic record
Abstract
Focus on evidence-based practice in the area of parole has been increasing in recent years.The purpose of the current study was to examine aspects of paroling authorities and how they function in order to better define high quality paroling systems.In order to achieve this, the Parole Quality Assurance Inventory (PQAI) was developed to measure paroling authority quality.The psychometric properties of this scale were evaluated, as well as, efforts were made to validate this scale by examining its relationship with parole performance indicators.Thirteen paroling authorities completed the PQAI.Results found that the PQAI was unable to be validated.Although, with a large amount of removed items, the scale was able to achieve appropriate psychometric properties, there was no relationship between PQAI subscale and total scores with the proportion of offenders who failed in the community.Limitations and future directions are discussed.I am grateful for the advisory panel which assisted with the development of the Parole Quality Assurance Inventory.Jean Sutton, Robbye Braxton, Cathy Banks, and Nancy Campbell provided valuable feedback which made this project all the better.As well, I would like to thank Keith Hardison for the assistance recruiting paroling authorities.It was certainly challenging at times, but I appreciated all help I could get.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".