Hunt a Killer: The Ludic Legacy of Aporia-Epiphany Dialectics in Jack the Ripper Media
Bibliographic record
Abstract
Abstract: Over the last century, the Victorian-era mystery case of Jack the Ripper has become synonymous with the gamified trope of solving a crime. This article pivots the discussion of the Ripper and his crimes away from film, literature, and television to look instead at how the infamous case has been repurposed in digital ergodic forms such as the online database Casebook: Jack the Ripper and two video games: Dance of Death: Du Lac & Fey (2019), by Salix Games, and Jack the Ripper (2015), the downloadable expansion to Ubisoft's Assassin's Creed Syndicate . These examples of ergodic media make use of differing aporia-epiphany (or problem-solution) dialectics that either allow amateur sleuths to "play" the game of identifying the killer, as the online database does, or offer players a chance to stop the Ripper in a digital Victorian space, as most Ripper video games do. Ultimately, as this article argues, Ripper video games often abandon game-like exploration into the Ripper's true, historical identity to instead focus on stopping the killer, perpetuating the mythic figure of the Ripper in the process.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".