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Record W4289531067 · doi:10.18778/2391-8551.08.01

16-bit dissensus: post-retro aesthetics, hauntology, and the emergency in video games

2022· article· en· W4289531067 on OpenAlexaff
Patrick R. Dolan

Bibliographic record

VenueReplay The Polish Journal of Game Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsYork University
Fundersnot available
KeywordsCLARIONAestheticsIdeologySociologyMedia studiesPoliticsPolitical scienceLawArtComputer science

Abstract

fetched live from OpenAlex

Santiago Zabala reveals a crisis in modern society that perceives a world dominated by oppressive neoliberal ideology as acceptable and unproblematic. He claims that today’s greatest emergency is that we fail to notice other emergencies in society. To break out of this state, we need an aesthetic force to shock individuals into a new awareness. Unfortunately, while many social and global issues have recently come to widespread attention, the emergency still prevails in many forms of media. For example, the emergency in AAA video games appears in their continual push for higher resolution graphics, hyper-detail, verisimilitude, and intricate gameplay, perpetuating a hegemonic ideology. Exploitative labor practices, lack of representation beyond hetero-sexual, cis-gendered and neurotypical, and capitalist ideals are perpetuated in popular games in service of a hyper-real, high-fidelity aesthetic. One force that combats this emergency is pixel graphics and simplified gameplay, or post-retro aesthetics. While tied to the past, these aesthetics are not nostalgic but transgressively hauntological. To explore this claim, I discuss Dys4ia and Undertale as key post-retro games and reach beyond commercial indie gaming to point to hauntological work being done through DIY game making platformers such as Bitsy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.328
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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