Real scary/scary real: Consuming simulated and authentic horrors in the digital era
Bibliographic record
Abstract
Snuff, like porn, has been challenged by feminist and other political debates around representations focused on the body, exaggerated performance, claims of ‘realness’ and concerns about representing and/or encouraging violence against women. Thus, it is not surprising that simulated snuff horror, as a subgenre, is heavily influenced by the same technological changes that have also affected the porn industry: the content of the videos, how the videos are produced and how they are consumed. I argue that the decontextualized digital context of media production and consumption has especially lent itself to the subgenre of horror I refer to as ‘simulated snuff films’ and aids in the longevity of snuff mythology. I use the terminology simulated snuff films to differentiate these fictional, from authentic snuff. Building on Steve Jones’ work, I explore the consumption of simulated snuff films that are scary real – fictional content that purposefully attempts to approximate the imagined look of a real snuff film – and films that are real scary – authentic depictions of extreme sexual violence and death – which may not give the appearance of being real or may be read by audiences as being faked. Further, using Jean Baudrillard’s theories of Simulation and Simulacra (1981), I argue that the case of Luka Magnotta, and his now infamous internet videos, exemplifies the hyperreality of snuff films in the post-9/11 context. To put it another way, simulated snuff films now appear more real than authentic recordings of murder in the digital sphere.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".