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Record W2154457055 · doi:10.1155/2010/897217

Time and Space in Digital Game Storytelling

2010· article· en· W2154457055 on OpenAlexaff
Huaxin Wei, Jim Bizzocchi, Tom Calvert

Bibliographic record

VenueInternational Journal of Computer Games Technology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsNarrativeStorytellingGame studiesGame designComputer scienceRepresentation (politics)Space (punctuation)Game mechanicsField (mathematics)Narrative criticismNarrative inquiryNarrative networkMultimediaArtificial intelligenceMathematicsPolitical scienceLiteratureArtPolitics

Abstract

fetched live from OpenAlex

The design and representation of time and space are important in any narrative form. Not surprisingly there is an extensive literature on specific considerations of space or time in game design. However, there is less attention to more systematic analyses that examine both of these key factors—including their dynamic interrelationship within game storytelling. This paper adapts critical frameworks of narrative space and narrative time drawn from other media and demonstrates their application in the understanding of game narratives. In order to do this we incorporate fundamental concepts from the field of game studies to build a game-specific framework for analyzing the design of narrative time and narrative space. The paper applies this framework against a case analysis in order to demonstrate its operation and utility. This process grounds the understanding of game narrative space and narrative time in broader traditions of narrative discourse and analysis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.014
Scholarly communication0.0090.011
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.260
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations51
Published2010
Admission routes1
Has abstractyes

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