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
<p>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, <em>ROI</em>=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 (<em>M</em>=46%); (3) physical exams (<em>M</em>=49%); (4) other tests (<em>M</em>=73%)]. Internet-based tests are simultaneously sensitive (97%), specific (97%), non-discriminatory, objective, inexpensive, $100/test, require 2-4 hrs.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".