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Record W4400886062 · doi:10.25686/2410-0773.2019.2.114

«ШКОЛЬНЫЕ СТРЕЛКИ» И ИХ МОТИВАЦИЯ: «СИНДРОМ КРЫСОЛОВА», «СИНДРОМ ГЕРОСТРАТА», «СИНДРОМ НИБЕЛУНГОВ»?

2019· article· en· W4400886062 on OpenAlexaboutno aff
С. А. Сергеев, A. S. Sergeev

Bibliographic record

VenueSocialʹnoe vremâ. · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Introduction. The article analyzes the phenomenon of armed attacks on educational institutions committed by students of the same educational institution (school shooters). This phenomenon is considered in a wider context of armed attacks in public places, but the focus is on the socalled rampage school shooting, when an armed criminal or criminals choose their victims at random, thereby attacking not certain individuals, but the institute itself (academic institution). Methods. The study is based on a comparative analysis of more than one hundred attacks on educational institutions in the United States, Canada, Western Europe and Russia. Results. Various theories that explain the behavior of school shooters are critically evaluated: organic brain damage, substance abuse, poor socialization, bad relationships with parents and peers, school bulling and the intention to take revenge. The authors proposed three syndromes that characterize various types of school shooters: Pied Piper syndrome (resentment of a society that did not evaluate or underestimate the merits and dignity of the individual), Herostratus syndrome (desire to become famous at any cost) and Nibelung syndrome (desire to destroy your opponents and yourself). Conclusions. Some recommendations for the prevention of armed attacks on educational institutions have been proposed. The authors believe that the most effective would not be to increase access control in schools and not to control the weapon, but to increase attention to the leakages of information from school shooters to social networks, their friends, relatives and acquaintances. Введение. В статье анализируется феномен вооруженных нападений на учебные заведения, совершаемые учащимися этого же учебного заведения ( школьные стрелки ). Данное явление рассматривается в более широком контексте вооруженных нападений в общественных местах, но основное внимание уделено так называемой неистовой школьной стрельбе , когда вооруженный преступник или преступники выбирают своих жертв случайным образом, тем самым нападая не на определенных лиц, а на сам институт (учебное заведение). Методы. Исследование основывается на сравнительном анализе более чем ста случаев нападений на учебные заведения в США, Канаде, странах Западной Европы и России. Основные идеи, результаты и обсуждение. Критически оценены различные теории, объясняющие поведение школьных стрелков : органическое поражение головного мозга, злоупотребление психоактивными веществами, слабая социализация, выражающаяся в плохих отношениях с родителями и сверстниками, школьная травля и намерение отомстить. Авторами предложены три синдрома, характеризующие различные типы школьных стрелков : синдром Крысолова (обида на общество, не оценившее или недооценившее заслуги и достоинства данного индивида), синдром Герострата (стремление прославиться любой ценой) и синдром Нибелунгов (стремление уничтожить своих противников и себя самого). Заключение. Предложены некоторые рекомендации по профилактике вооруженных нападений на учебные заведения. Авторы считают, что наиболее эффективным было бы не усиление пропускного режима в учебных заведениях и не контроль за оружием, а повышение внимания к утечкам информации от школьных стрелков в социальные сети, их друзьям, родственникам и знакомым.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.012

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.

Opus teacher head0.028
GPT teacher head0.387
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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