Finding High-Risk Adults/Youth: Assessing Risk Management (ARM): Ask-Standard-Predictor (ASP): Reliable, Valid, 11-Question Survey: Finding High-Risk Saves USA $300B-$1.2T Annual Violence Expense & Prevents 10,000 Murders
Bibliographic record
Abstract
Summary. - Ask-Standard-Predictor (ASP) surveys high-risk adults/youth before high-risk/self-harm [assessing risk management (ARM)]; 2,722 (1,595 adults, 1,127 youth) followed (in court, hospital, physician, school records) for 3-12-years, analyzed with robust, statistical methods (in-bag, out-of-bag, cross-validation, Shao’s bootstrapped, logistic-regressions), resulting in:: (a) predictive-accuracy [area-under-curve (AUC) = 0.99, adults, AUC = 0.91, youth, AUC = 0.96, combined]; (b) internal-consistency [Cronbach’s α = 0.61–0.62]; (c) test-retest-reliability [rtt = 0.75–0.76]; (d) convergent-divergent-validity (ability, achievement, perception, personality tests); (e) replicated-sensitivity- specificity [96-97%]. Predictors are valid across many populations [including 311,599 targeted, Chicago youth, receiving jobs, mentors, and anger-training, over 17-years, saving $3.6B-$5.3B, 1,242 homicides, 46% less shootings, and 77 fewer violent-youth-crimes] in “1-Chicago-Youth-Summer-Jobs-Program.” ASP is an extension of 95-year-probation-parole-decision-making-tests, allowing lowering high-risk by targeting scientifically-proven, cost-effective treatments. ASP and MMPI-2/A have the same”7-point-high-risk-profile” for youth, adults, males, females, homicidal, mass-murdering, serial-killing, sex-offending, suicide-completers, and overdosing, namely: 1-violence, 2-deception, 3-depression, 4-antisocial-behavior, 5-paranoid-ideation, 6-schizophrenic-thinking, 7-addiction-alcoholism.
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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 | 0.039 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".