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Record W4404086437 · doi:10.1177/15554120241282079

Unreal Games

2024· article· en· W4404086437 on OpenAlexafffund
­Carl Therrien, Jean-Charles Ray, Laurie-Mei Ross Dionne, Fabienne Sacy

Bibliographic record

VenueGames and Culture · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à MontréalUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

Throughout the history of game studies, scholars have reflected on the essential features of games. In the past decade, these discussions have extended beyond academia to the broader gaming community, where certain genres have been elevated as "real games." Walking simulators, dating sims, visual novels, and hybrid forms are often at the center of these debates, criticized for their simplicity, lack of challenge, or sometimes overtly sexual content. Despite this, the popularity of these genres is undeniable: since 2007, over 50,000 titles have been documented on the Visual Novel Database, and thousands of walking simulators are available on Steam and other platforms. Many of these titles also appear on MobyGames and have been referenced as "games" throughout history. In this article, we delve into this vast and often overlooked corpus to examine the qualities that can elevate gameplay to unreal levels.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.888
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.287
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes2
Has abstractyes

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