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Record W3049197894

Slender Man on Trial: Has Media Taken the Minds of the Young?

2016· article· en· W3049197894 on OpenAlexaff
Frances E. Chapman, Lauren Tarasuk

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsLakehead University
Fundersnot available
KeywordsCommitPrisonCriminologyMonsterFolkloreHomicideLawOrder (exchange)Character (mathematics)PsychologyPolitical scienceHistoryArtLiteraturePoison controlSuicide preventionArt history
DOInot available

Abstract

fetched live from OpenAlex

Abstract On Saturday May 31, 2014, 12-year-old Morgan Geyser and Anissa Weier stabbed their young classmate, Payton Leutner, nineteen times and left her for dead. Police apprehended Weier and Geyser charging them with attempted first-degree intentional homicide, which carries a prison sentence of up to sixty-five years. Why would these very young girls commit such a brutal crime? Both of the girls told police that they committed the stabbing in order to become “proxies” of “Slender Man,” a fictional monster. It is anticipated that Slender Man will be a central character in the trials of the young Wisconsin girls. This paper explores the potential criminal and civil liability that the creators of the Slender Man folklore might face by looking back at several cases from the 1990s, including the criminal defense of “television intoxication,” and several civil cases suing media artists for their suicide/murder attempts. Subliminal messaging will be explored as well as the ramifications there may be for Geyser and Weier. It is unknown if a civil claim will be launched, and the path that the Slender Man criminal case will follow is yet to be determined. What we do know is that in December, 2014, the girls were found competent to stand trial as adults despite assertions from the girls that they see unicorns, have mind control powers, and that Slender Man is real.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.231
Teacher spread0.204 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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