Gaming with Ghosts: Hauntology, Metanarrative, and Gamespace in Video Games
Bibliographic record
Abstract
This thesis undertakes a hauntological analysis of video games and their ludonarrative structures.It argues that video games are haunted by what Jacques Derrida (1994) refers to as spectres, or figures that undo the ontological assumptions of time, space, and being in the world.Video games are considered haunted by spectres both within and external to their diegetic content, including the gamer communities that emerge in response to a game's narrative.Central to this analysis is McKenzie Wark's (2007) concept of gamespace, or the material world, and Richard Grusin's (2004) concept of premediation and the ways new media generate multiple possible futures in an attempt to insulate the present moment from traumatic, unforeseen ruptures.Chapter One proposes a premediated hauntology of video games, arguing that the ability to manipulate time and space in games like Jonathan Blow's Braid (2008) generates digital, algorithmic, and allegorithmic spectres and avatars.Algorithmic spectres simultaneously premediate future playthroughs and attempt to remedy a present moment that is haunted by the ghosts of past premediations, and in so doing represent the radical potential of similar movements through gamespace.Chapter Two shifts from primarily narrative-driven analysis to what is termed "metagamespace," or how algorithmic spectres like The Elder Scrolls III: Morrowind's non-playable character Vivec mutate the wall between game and gamespace but, by enacting such metanarrative, demonstrate the limits of a narrative-driven hauntological mode.A revolutionary figure within Morrowind's diegesis, Vivec represents not only the necessity of overturning the assumed relationship between game and gamespace, but also the impossibility of doing so from within the game itself.Because the game is meant to
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".