Wider Computer-Test Use to Assess-Risk-Manage (ARM) Cuts U.S. Yearly 10,000-Violence-Deaths &Saves $322B-$1.2T-Violence-Cost-by Diverting High-Risk-Persons
Bibliographic record
Abstract
Summary.—Ask Standard Predictor (AS) and Minnesota Multiphasic Personality Inventory (MMPI-2/A) computer-tests find high-risk persons with 3-or-4 out of a “7-point-high-risk-profile”(addiction-alcoholism, deception, depression, antisocial-behavior, paranoid-ideation, schizophrenic-thinking, violence), based on 320,051-persons, 212-studies, 95-years, with 97% objectivity, reliability, sensitivity, specificity, validity, compared to current ways that miss 61% (homicidal, mass or serial-murdering, sex-offending, overdosing, suicide-completers), costing U.S. $2.36T/year. One solution is teaching insurance executives to copy in 100 cities, “1-summer-Chicago-youth-job-program” 311,599 high-risk youth, over 17-years, targeted with ASP-replicated-equation, diverted with jobs, mentors, anger-training [ROI = $6.42/$], saving $3.6B-$5.3B, preventing 1,242 homicides, lowering shootings 46%, violent offenses 77%. Another solution is weekend, business-university-department workshops on the math and science with 124 real-life-stories of finding-high-risk-persons and taking the 2 computer tests for leadership (education, energy, health, military, nonprofit-religious, police, prisons, transportation, ROI = $2-323/$) lowering high-risk, saving hundreds of billions. Insurance-brokers mandating workshop attendance in professional liability contracts lower victim payouts, lost profits, and trauma.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".