Finding High Risk Persons with Internet Tests to Manage Risk—A Literature Review with Policy Implications to Avoid Violent Tragedies, Save Lives and Money
Bibliographic record
Abstract
The goal is to share policy implications of sensitive, specific internet-based tests in place of current approaches to lowering violence, namely fewer mass murders, suicides, homicides. When used, internet-based tests save lives and money. From 2009-2015, a Chicago field test had 324 fewer homicides (saving $2,089,848,548, ROI=6.42). In 60 yrs., conventional approaches for high risk persons (e.g.,. inappropriately releasing poor, severely mentally ill) led to unnecessary expense including yearly: (a) 300 mass murders (59% demonstrating psychiatric conditions); (b) 1-6% having costly personnel challenges; (c) 2,100,000 “revolving door” Emergency-Room (ER) psychiatric admissions (41,149 suicides, 90% mentally ill); (d) 10,000,000 prisoners (14,146 homicides, 20% psychiatric challenges). Current metrics fail [success rates from 25%-73%: (1) for background checks (25%); (2) interviews (M=46%); (3) physical exams (M=49%); (4) other tests (M=73%)]. Internet-based tests are simultaneously sensitive (97%), specific (97%), non-discriminatory, objective, inexpensive, $100/test, require 2-4 hrs.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".