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Record W2572349085

Chronologically nonlinear techniques in traditional media and games.

2014· article· en· W2572349085 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFoundations of Digital Games · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsCarleton University
Fundersnot available
KeywordsToolboxAgency (philosophy)Computer scienceNonlinear systemQuality (philosophy)MultimediaSociologyEpistemologySocial science
DOInot available

Abstract

fetched live from OpenAlex

Although stories in games have become more sophisticated over time, their use of nonlinear techniques has not yet become as prevalent as in traditional media like novels and films. Writers have largely excluded nonlinear techniques from their toolbox, possibly because of fears of introducing inconsistencies when player actions alter past events. However, as we show through a survey of common nonlinear techniques seen in television, novels, and film, games can and have avoided these inconsistencies while maintaining gameplay agency. Many players prefer a high quality static story incorporated into strong gameplay, making the insight from this discussion immediately useful in designing nonlinear game stories. We also discuss some ways in which nonlinear techniques can offer both gameplay and story agency, hopefully bringing the quality of game stories one step closer to their traditional counterparts.

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.

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)0.001
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: none
Teacher disagreement score0.900
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.286
Teacher spread0.253 · 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