A Review of the Costs of Assault, Homicide, Mass Murder and Pedophilia with Implications for the Insurance Industry and the U.S. Roman Catholic Church: A Rationale for Using Computer Tests and Machine Learning Equations
Bibliographic record
Abstract
20 May 2021 U.S. dollar cost for assault = $33,773.52; homicide, $3,834,988.08, domestic terror, mass murder, spree-shooting, 3 dead, $11,504,964.24, pedophilia, $139,430.28. Over 85 years, domestic-terror, mass-murder, spree-shooter assaults cost = 1 to 5,000. $33,773.52 x 5,000 = $168,867,600; dead victims = from 1 to 1,000. $3,834,988.08 x 1,000 = $3,834,988,080. 1936-2021 U.S. insurance industry mass murder costs = [$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] =$20,353,269,317.93. Projecting 2021 to 2105 insurance industry no policy change (i.e., computer tests, machine learning equations), $40,706,538,616.66, 3,330 deaths, 6,246 injuries, 388 suicides. U.S. Roman Catholic Church pedophilia costs, 1986-2011, $2,486,898,000, payouts + lost-donations [($2,486,898,000.00 x 1.3) = $3,232,967,400 = $5,719,865,400 + 5,679 x 5 = 28,395 victims]. Projecting 2017 U.S. Church (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.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".