Impact of risk assessment instruments on rates of pretrial detention, postconviction placements, and release: A systematic review and meta-analysis.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Many agencies use risk assessment instruments to guide decisions about pretrial detention, postconviction incarceration, and release from custody. Although some policymakers believe that these tools might reduce overincarceration and recidivism rates, others are concerned that they may exacerbate racial and ethnic disparities in placements. The objective of this systematic review was to test these assertions. HYPOTHESES: It was hypothesized that the adoption of tools might slightly decrease incarceration rates, and that impact on disparities might vary by tool and context. METHOD: Published and unpublished studies were identified by searching 13 databases, reviewing reference lists, and contacting experts. In total, 22 studies met inclusion criteria; these studies included 1,444,499 adolescents and adults who were accused or convicted of a crime. Each study was coded by 2 independent raters using a data extraction form and a risk of bias tool. Results were aggregated using both a narrative approach and meta-analyses. RESULTS: = .020). However, after removing studies with a high risk of bias, the results were no longer significant. CONCLUSIONS: Although risk assessment tools might help to reduce restrictive placements, the strength of this evidence is low. Furthermore, because of a lack of research, it is unclear how tools impact racial and ethnic disparities in placements. As such, future research is needed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it