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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".