Slender Man on Trial: Has Media Taken the Minds of the Young?
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".