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Record W4366393097 · doi:10.1177/15554120231167017

To the Center of Nowhere: Deep Mapping Digital Games’ Paratextual Geographies

2023· article· en· W4366393097 on OpenAlexaff
Jon Saklofske

Bibliographic record

VenueGames and Culture · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsAcadia University
Fundersnot available
KeywordsTextualityComputer scienceGame studiesRelation (database)Game mechanicsSociologyMultimediaArtificial intelligenceArtLiterature

Abstract

fetched live from OpenAlex

Games are possibility spaces and the experiential circumstances generated by the execution of digital game code are paratextual thresholds of transition and transaction. If games are paratexts, game design is a form of modeling; game-playing is a form of transactional mapping; and the critical engagement with games is best approached via a modified version of “deep mapping,” a digital humanities process indebted to textual studies approaches. This paper will explore the paratextual idea of deep mapping in relation to Hello Games’ constantly evolving No Man's Sky, and The C64, a full-sized reissue of Commodore 64 hardware by Retro Games Ltd. Overall, the use of deep mapping to comprehend the paratextual thresholds of games validates the importance of textual studies approaches, acknowledging the complex relationship between physicality and virtuality while also reconfirming that games require methodologies that move beyond reading paradigms and the exclusive framework of textuality.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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
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
Published2023
Admission routes1
Has abstractyes

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