16-Questions to Find Mass-Murderers, Spree-Shooters, Domestic-Terrorists, and a Study-1 of 232-School-Shooters with Controls and a Study-2 of 6-Teen-Shooters With 11-Homicidal and 12-Control Youth Rated with Ask Standard Predictor (ASP) of Violence Potential-Youth Version and the MMPI-A: Implications: Use Computer-Tests and Machine-Learning-Equations to Lower Insurance-Premiums and Prevent Church-Bankruptcy from Violent Offenses
Bibliographic record
Abstract
630 Domestic-Terrorist-Mass-Murdering-Spree-Shooters are compared with 623-controls and separated by16-Questions with significant a=.846, p<.01, AUC=.704, p<.01 that are: (1) homicidal? (2) suicidal? (3) stressful-life-event? (4) handgun-many-weapons-access? (5) violence-planning-preparing? (6) revenge-motive? (7) eliciting-others-concern? (8) intent-leakage? (9) criminal-misconduct-history? (10) personal-grievance? (11) random-violent-behavior? (12) threatening-victims? (13) dead-male-victim? (14) targeting-person-school-or-work? (15) student-professional-work-relationship? and (15) student? Before the killing, terrorists come twice to courts, doctors, schools and human resources and are not diagnosed as dangerous. In Study-1: [from 1936-2019] 232-school-shooters are contrasted with 232-controls resulting in 414-dead, 832-injured, and 68-suicides (29%) which are analyzed with logistic-regression, F= 227.14, p<.01, df=8/455, R=.894, p<.01, R2=.8, p<.01, and separated with 8-Questions: (1) student? (2) suicidal? (3) stressful-life-event? (4) homicidal? (5) violence-planning-preparing? (6) personal-grievance? (7) handgun-access? and (8) targeting-person(s)? In Study-2: 6-teen-shooters with 11-homicidal and 12-controls are contrasted with the Ask-Standard-Predictor [ASP] Violence-Potential, Youth-Version (54-questions, a=.61, p<.01, AUC=.91, p<.01, rtest-retest=.75, p<.01, F=123.09, p<.01, and the Minnesota Multiphasic Personality Inventory Adolescent Version [MMPI-A (468-questions):], ANOVA-F=17.22, p<.01, Lie, F=33.91, Depression, F=26.18, p<.01, Psychopathic-Deviate, F=57.45, p<.01,Paranoia, F=23.92, p<.01, Schizophrenia, F=21.69, p<.01, MacAndrews Alcohol, F=16.84, p<.01, Addiction Admission, F= 38.88, p<.01, resulting in a “7-point-violence-profile,”found over 95 yrs. in 212-studies .(N=320,051). The expense side includes 2 examples. 1st, School-shooter insurance-industry higher-premiums from (1936-2019) resulted in [414-dead @ $3,834,988.08=$1,587,685,065.12] + [832-injured @ $33,773.52=$28,099,568.64]+[232-shooters@$3,834,988.08=$889,717,234.60]+[$2,505,501,868.32x1.3= $3,257,152,428.82] =a high cost of $5,762,654,297.14]. With no-computer-tests-equations from 2020-2106, ($5,762,654,297.14x2= [the expense will double to] $11,525,308,594.27, 828-dead, 1,664-injured. The 2nd violence example is the U.S-Catholic-Church-pedophilia-loss, (1936-2107) [payouts, $17,435,353,000] + [lost-donations =1.3 x payouts=] 22,665,958,900=$40,101,511,900(1986-2107), with 5,679 victims increasing (1936-2107) to 39,753-victims.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 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".