Bibliographic record
Abstract
Although mass murder is a rare event in the United States—perhaps a dozen to a dozen and a half incidents a year in the recent decades—occurrences tend to overshadow the much greater number of other murders, because of the electrifying effect upon the public of so many lives being lost all at once. Much of the heightened frequency and greater death toll stems from the easier availability of semiautomatic weapons since the 1970s. Several recent, highly dramatic mass murders were committed by mentally ill persons, which has led to unwarranted stigmatization of the mentally ill as an inherently dangerous element in society. Mass murder is an almost exclusively male phenomenon (male:female ratio ∼24:1)—a reflection of evolutionarily driven tendency for males to be more aggressive than females. Most mass murders are planned well in advance of the outburst, usually as acts of revenge or retribution for perceived slights and wrongs. Overwhelming hopelessness is often present: this may help explain how nearly half the persons committing mass murder either commit suicide or are killed by the police in the immediate aftermath of the event. The percentages of mass murder among white and black persons approximate their percentages in the general population; the ratio for Hispanics appears less than expected. The majority of mass murderers are persons with paranoid personality configurations (including, at the more severe end, paranoid schizophrenia)—typically associated with a deep sense of disgruntlement and unfairness. Persons at high risk to commit mass murder are hard to spot in advance, given the much greater number of grudge-holding persons than those who ever carry out a mass murder. This complicates the task of law enforcement: Mass murder is difficult to prevent, all the more so given the unpopularity of government confiscation of semiautomatic weaponry.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".