Bibliographic record
Abstract
This research investigates the figure of the player as a pathogen agent able to impose a propagating form of homologation within the video game Watch Dogs: Legion (Ubisoft 2020). The study involves the analysis of the role of the player according to a multiple configuration: as patient zero, parasite, and epidemic agent. The role of the player is potentially expressed through his or her being a starting point of contagion, a parasite that raids the bodies of non-player characters (NPCs) and an epidemic agent—a potentially uncontrolled transmission medium. Watch Dogs: Legion is set in a totalitarian London of a hypothetical future in which the player can impersonate any NPC in the game world. Since this contagion effect is the fundamental mechanic of the experience, this paper shows how it is not the NPC to represent and be a subject to an idea of homologation but how it is the player who imposes a propagating form of homologation with his or her tastes, political, and social behaviours. Regarding the game narrative framework, it is possible to state that the totalitarian form implemented in the game design is addressed by the player across an epidemic process and expression. Compared to the trend of video games to homologate the player to ideologies and mechanics of the product, the analyzed text proposes an inverse process. The paper argues that as an epidemic agent, the player both generates and fights (as a network) a process of homologation, creating kindred avatars and tackling totalitarianism.
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 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 | 0.003 |
| 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 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".