The Strengths/Structured Assessment for Youth (S/SAY): Evaluating Strengths in a Case Study of a Justice-Involved Youth
Bibliographic record
Abstract
Risk assessment is a priority in many juvenile justice systems. We examine the present methods available for evidence-based assessment of justice-involved youths, demonstrating their focus on risk factors, and then outline a strengths-based approach, including a discussion of conceptual and methodological challenges in the evaluation of the positive aspects of justice-involved youths. The advantages of the first structured assessment instrument to focus on the strengths of justice-involved youths – the Strengths/Structured Assessment for Youth (S/SAY) – are then explored through analysis of a case study. The implications for the use of the S/SAY in evaluation and intervention with justice-involved youths are discussed, particularly its ability to provide more accurate assessment and encourage intervention. The importance of obtaining a balanced understanding of both strengths and risk factors is emphasized.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".