Bibliographic record
Abstract
The Service Planning Instrument (SPIn; Orbis Partners, 2003) and its derivatives were designed to assess a range of risks, needs, and strengths among justice-involved adults for the purpose of guiding classification and informing case management in both community and custody settings. The Service Planning Instrument for Women (SPIn-W; Orbis Partners, 2006) was specifically introduced as a gender-responsive protocol for justice-involved females, while SPIn Re-entry (Orbis Partners, 2013) is an abridged version of the larger instrument that has been tailored to re-entry populations. Based on large community and custody samples across Canada and the United States, the family of SPIn tools has evidenced moderate to high levels of predictive accuracy in forecasting general reoffending, violent reoffending, and technical violations, with AUCs attaining .78. A defining feature of the assessment model is the quantitative inclusion of strengths. Validation data collected to date demonstrate that SPIn strength scores contribute incrementally to the prediction of recidivism over and above the consideration of risks and needs alone. Beyond improving predictive validity, the inclusion of strengths in the assessment protocol also results in an enhancement of case management practices to reinforce a collaborative approach to developing and pursuing case plans.
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.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.013 |
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".