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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.465 | 0.263 |
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".