Trial-Lawyer Leaders in Court Change Institutions Forcing the Use of Tests and Equations So That Challenged Ones Become Successfully Healthy Thus Safeguarding Schools and Workplaces from the High-Risk Homicidal, Mass-Murdering and Sex-Offending
Bibliographic record
Abstract
Annual U.S. violence expense = $3.46 trillion (12.5% GDP). Insurance mass-murder/sex-offending payouts range from $3M-$1.1B. The U.S. Catholic Church has 24/194 (13%) pedophilia-bankrupt dioceses. Using 4-machine learning tests-equations, 7-high-risk predictors are—(1) addiction-alcoholism, (2) antisocial-behavior, (3) deception, (4) depression, (5) paranoia, (6) schizophrenic-thinking, (7) violence-potential—found in 212 studies (320,051 persons); in anticipating violent behavior, 4 tests-equations have 97% impressive predictive accuracy, ASP (Ask Standard Predictor, 2010), BASC (Behavior Assessment System Children, 1992), CAPI (Child Abuse Potential Inventory, 1986), MMPI-2/A (1992). Using tests-equations, over 16-years, insurance-leaders targeted 255,806 high-risk youth with cost-effective, (ROI=$6.42 for every dollar spent) jobs, anger-training, mentors, showing substantially 1,070 less homicides ("Chicago Summer-1 program"). This proves tests-equations with interventions work. Trial lawyer leaders using tests-equations with interventions motivate institutional change by increasing homicide, mass-murder, sex-offending settlements-awards to $10-$100B leading insurance professionals to modify liability contracts mandating continuing professional education in test-equation use, thus lowering premiums, bankruptcies.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.133 | 0.037 |
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".