Study 1: 630 Domestic-Terrorist, Mass-Murdering, Spree-Shooters Differ from 623 Controls and Study 2: 15 Domestic-Terrorist, Mass-Murdering, Spree-Shooters Differ From 23 Homicidal and 36 Controls on the Standard Predictor of Violence Potential and the MMPI-2/A: Implications Are to Use Computer Tests and Machine Learning Equations
Bibliographic record
Abstract
Study 1: 630 spree-shooters [1936-2021] (1,650 deaths; 3,123 injuries; 194 suicides [31%]), 623 controls logistic regression differences (F= 260.44, df=10/1242, R=.82, R2 =.68, p<.01): (1) homicidal ideation; (2) planning-preparation; (3) stressful-life-event; (4) revenge-motive; (5) acquired-multiple-weapons; (6) elicited-concern; (7) school-location; (8) personal-grievance; (9) suicide; (10) current-student. Study 2: 15 spree-shooters differences, adult, teen: [SP] violence (F=17.48, 123.09); [MMPI-2/A] infrequency (F=92.15, 17.22); lie (F=13.13, 33.91); depression (F=37.76, 26.18); psychopathic-deviance (F=44.66, 57.45); paranoia (F=50.58, 23.92); schizophrenia (F=53.85, 21.69); alcohol (F=42.01, 16.84); addiction (F=57.34, 38.88) compared with 23 homicidal, 36 controls. Spree-shooter loss (1936-2021) = [$2,416,042,490 (630 @ $3,834,988.08) + $6,327,730,332 (1,650 @ $3,834,988.08) + $105,474,702.96 (3,123 @ $33,773.52) = $8,849,247,525.36] + [insurance, tax-increases $11,504,021,782.97 ($8,849,247,525.36 x 1.3] =$20,353,269,308.33. Projecting 2021 to 2105 insurance industry with no computer tests, machine learning equations, $40,706,538,616.66, 3,330 deaths, 6,246 injuries, 388 suicides. Projecting 2017 U.S. Church pedophilia loss (2012-2037, 2038-2056, 2057-2082, 2083-2107), $5,719,865,400 x 5 = $28,599,327,000, 5,679 x 5 = 28,395 victims.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".