16-Questions to Find Domestic-Terrorists, Mass-Murderers, Spree-Shooters, With a Study-1: 370-Workplace-Shooters vs. 370-Controls, and a Study-2: 9-Adult-Shooters With 12-Homicidal and 24-Controls Rated on the Ask Standard Predictor of Violence Potential-Adult Version and the MMPI-2: Implications Are to Use Computer-Tests and Machine-Learning-Equations to Lower Insurance Premiums and Prevent Church Bankruptcy
Bibliographic record
Abstract
630 Terrorist-Mass-Murdering-Spree-Shooters are compared with 623-controls and separated by 16-Questions with 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? (7) eliciting-others-concern? (8) intent-leakage? (9) criminal-misconduct? (10) grievance? (11) random-violent-behavior? (12) threatening-victims? (13) dead-male-victim? (14) targeting-person-school-work? (15) student-professional-work-relationship? and (16) student? Before killing, terrorists come twice to courts-police, doctors-hospitals, schools-universities and human resources and are not diagnosed as dangerous due to error-prone current ways. In Study-1: 370-workplace-shooters (1968-2021) are contrasted with 370-controls using logistic-regression (F= 134.64, p<.01, df = 13/726, R=.84, p<.01, R2=. 71, p<.01 resulting in 14-Questions: (1) homicidal? (2) intent-leakage? (3) stressful-life-event? (4) revenge? (5) many-weapons? (6) elicited-others-concern? (7) criminal-misconduct? (8) threatened-victims? (9) dead-male-victim? (10) targeted-workplace? (11) professional-work-relationship? (12) suicidal? (13) random–violence? In Study-2: 9-spree-shooters are distinguished from 12-homicidal and 24-control adults showing a “7-point-violence-profile on two scales: (1)[Ask Standard Predictor of Violence Potential-Adult Version] violence (F=17.48, p<.01); and (2) the Minnesota Multiphasic Personality Inventory, Second Edition [MMPI-2] F (infrequency) (F=92.15, p<.01); L (lie) (F=13.13, p<.01), (3) D (depression) (F=37.76, p<.01); (4) Pd (psychopathic-deviance) (F=44.66, p<.01); (5)Pa (paranoia) (F=50.58, p<.01); (6) Sc (schizophrenia) (F=53.85, p<.01), (7) MacAndrews alcohol (F=42.01, p<.01); AAS (addiction admission) (F=57.34, p<.01). Looking from 1968-2021 at the insurance industry expense, there is the workplace-shooter loss = [$1,418,945,589.60 (370-shooters @ $3,834,988.08) + $4,053,582,400.56(1,057-deaths @) $3,834,988.08 + $37,556,154.24 (1,112-injured@ $33,773.52)] = $5,510,084,144.40 + [higher-insurance-premiums [$5,510,084,144.40 x 1.3 =] $7,163,109,387.72 = $12,673,193,582.12. No-computer-tests-equations, 2022-2105 [2 x $12,673,193,582.12 = $25,346,387,064.24.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 the 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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".