An exploration of the symmetry between crime-causing and crime-reducing factors: Implications for delivery of offender services.
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
Both the Risk-Needs-Responsivity (RNR) and Structured Professional Judgment (SPJ) risk assessment approaches assume that a strong relationship exists between crime-causing and crime reducing factors. Using a probation sample, the present article examines whether crime-causing and crime-reducing factors correspond. Probationers completed questionnaires where they were asked what factors were crime-causing and what factors were crime-reducing. Overall, the relationship between the crime-causing and crime-reducing factors was very weak-even after ruling out potential measurement and methodological artifacts (i.e., internal consistency, item stability, and acquiescent responding). Applied to an individual offender, the results suggest that conducting assessments and recommending interventions need not be bound by assumptions that risk factors for past crime must be targeted to reduce crime. New endeavors to develop causal and idiographic crime-reducing strategies warrant consideration. (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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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