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
The application of common risk assessment measures, such as the Level of Service Inventories (LSI), to Aboriginal offenders has been a criticized practice. The belief that Aboriginal offenders have distinct needs has informed the argument that existing risk-need assessments cannot adequately capture their risk. To explore this, the present meta-analysis reviewed 16 samples to test the extent to which LSI scores predict recidivism for Aboriginal compared with non-Aboriginal offenders. In addition, one large sample was used to examine the similarities in recidivism rates per LSI score for Aboriginal and non-Aboriginal offenders. Results indicated that the LSI predicts recidivism for Aboriginal offenders; however, for five of eight subscales, it predicts with less accuracy compared with non-Aboriginal offenders. In addition, the LSI underclassifies low-scoring Aboriginal offenders, but accurately estimates recidivism rates for higher scoring offenders. Implications for research into culturally-specific risk factors and the application of current risk factors to Aboriginal offenders are explored.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 0.002 |
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