(Always) Playing the Camera: Cyborg Vision and Embodied Surveillance in Digital Games
Bibliographic record
Abstract
As the increasingly ubiquitous field of surveillance has transformed how we interact with each other and the world around us, surveillance interactions with virtual others in virtual worlds have gone largely unnoticed. This article examines representations of digital games’ diegetic surveillance cameras and their relation to the player character and player. Building on a dataset of forty-one titles and in-depth analyses of two 2020 digital games that present embodied surveillance camera perspectives, Final Fantasy VII Remake (Square Enix 2020) and Watch Dogs: Legion (Ubisoft Toronto 2020), I demonstrate that the camera is crucial in how we organize, understand, and maneuver the fictional environment and its inhabitants. These digital games reveal how both surveillance power fantasies and their critique can coexist within a space of play. Moreover, digital games often present a perspective that blurs the boundaries between the physical and the technically mediated through a flattening of the player’s “camera” screen and in-game surveillance cameras. Embodied surveillance cameras in digital games make the camera metaphor explicit as an aesthetic, narrative, and mechanical preoccupation. We think and play with and through cameras, drawing attention to and problematizing the partial perspectives with which worlds are viewed. I propose the term cyborg vision to account for this simultaneously human and nonhuman vision that’s both pluralistic and situated and argue that, through cyborg vision, digital games offer an embodied experience of surveillance that’s going to be increasingly relevant in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".