Running head: GENETIC, PEER, AND ENVIRONMENTAL INFLUENCES ON PEOPLE An Understanding of How Peer, Genetic, and Environmental Influences Can Motivate Terrorists or Ordinary People to Kill Themselves and Others.
Bibliographic record
Abstract
Suicidal ideation in America is not an unusual behavior among its population. Nearly every week, stories of people who attempt to give an end to their lives travels around the newspapers and the media. In fact, suicide is the third leading cause of death among teens in the Unites States (Balhara, Dhawan, & Natasha, 2007). However, there are more extreme cases where people’s emotional instabilities are displaced to others. These highly seldom occurrences can be caused by several different factors or the combination of those. Factors can include genetic predispositions, peer and environmental influences, and even the culture a person is raised in. Shaping a person’s personality, those influences have the power to not only cause harm to the person, but also to affect others. For instance, real life events like the 1999 Columbine High School massacres in Littleton, Colorado and the 2006 Dawson College incident in Montreal, Canada were mostly caused by certain persons ’ personality imbalances and influences from their lifetime experiences. Those very sad and disturbing events will help understand how a person’s motivations are related to genetic and environmental influences. The morning of April 20th, 1999 was not a usual day at Columbine High School in Jefferson County, Colorado. At 11 a.m., two students, Eric Harris and Dylan Klebold arrived at
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.086 | 0.008 |
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".