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Record W3091964736 · doi:10.3390/arts9040103

Film Adaptation as Experimental Game Design

2020· article· en· W3091964736 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

VenueArts · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsConcordia University
Fundersnot available
KeywordsAdaptation (eye)Game designNarrativeGenerative grammarComputer scienceMovie theaterSet (abstract data type)Video gameSelection (genetic algorithm)Process (computing)Human–computer interactionEngineering design processArchitectural engineeringAestheticsMultimediaVisual artsArtificial intelligenceArtEngineeringPsychologyMechanical engineeringLiterature

Abstract

fetched live from OpenAlex

Film adaptation is a popular approach to game design, but it prioritizes blockbuster films and conventional “game-like” qualities of those films, such as shooting, racing, or spatial exploration. This leads to adaptations that tend to use the aesthetics and narratives of films, but which miss out on potential design explorations of more complex cinematic qualities. In this article, I propose an experimental game design method that prioritizes an unconventional selection of films alongside strict game design constraints to explore tensions and affinities between cinema and videogames. By applying this design method and documenting the process and results, I am able both to present an experimental set of videogame film adaptations, along with potentially generative design and development themes. In the end, the project serves as an illustration of the nature of adaptation itself: a series of pointed compromises between the source and the new work.

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)0.000
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: none
Teacher disagreement score0.923
Threshold uncertainty score0.703

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)0.0000.001

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 head0.111
GPT teacher head0.326
Teacher spread0.215 · 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

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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