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Record W4410313908 · doi:10.32920/ifmj.v5i1-2.2333

Code-Driven Narratives

2025· article· en· W4410313908 on OpenAlexvenueno aff
Roderick Coover

Bibliographic record

VenueInteractive Film and Media Journal · 2025
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeProgramming languageCode (set theory)Computer scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Through combinatory systems, generative creation and structured constraints, code-driven cinema challenges enduring narrative structures, altering both creative processes and viewer expectations. The methods introduce unpredictability and ambiguity, engaging audiences in meaning-making processes while also helping to illuminate complex relationships between humans and machines. Intertwining past, present, and future, code-driven films pose questions of time, memory, place and story. In combinatory cinema, also known as database film, algorithms select elements from pre-recorded fragments from a database of clips and sounds, and in generative cinema, original media is created by code. Unlike interactive cinema, which privileges user choice, these forms often emphasize chance, generating ambiguous and fluid meanings through montage, compositing and other forms of aleatory juxtapositioning. Drawing from avant-garde traditions such as Dada and Surrealism, as well as constraint-based artistic practices like those of the Oulipo literary group, combinatory and generative cinema leverages randomness and hidden parameters, exposing latent narrative structures. The tension between fragmentation and coherence in these systems challenges linear expectations in novel ways. The paper offers five examples that demonstrate diverse approaches to code-driven filmmaking, particularly through combinatory and generative methods. Three Rails Live (Coover, Rettberg and Montfort 2013) employs randomized sequences and poetic combinatory structures which create an ever-evolving cinematic experience. Toxicity: A Climate-Change Narrative (Coover and Rettberg, 2016) intertwines fictional and nonfictional elements using a combinatory system that offer possibilities of hope and survival in the face of climate catastrophes. Penelope (Coover and Rettberg 2019) generates algorithmically structured poetry from a database of textual, visual, and auditory fragments, evoking themes of memory, loss and migration. It will happen here (Coover, Vidiksis and Montfort 2021) and its live performance variant, The Floods, integrate documentary footage with generated text and sound, creating immersive, site-specific experiences that immerse viewers in experiences of environmental change and collective memory. Lastly, Water on the Pier (Coover, Vidiksis and Montfort 2021) employs live environmental data and interactive elements to situate and personalize climate urgencies. These works highlight the possibilities of combinatory and generative filmmaking to expand the interpretative capacities of audiences and creators alike. Further, the approaches enrich collaboration between filmmakers, writers, and composers by offering nonhierarchical, iterative, and responsive processes of production and re-imaginings of narrative structures. The paper argues that combinatory and generative cinema and related code-based methods not only reveal the mechanics of technological storytelling but also interrogate how industrial and post-industrial paradigms shape cultural narratives. By disrupting linearity, these systems offer new approaches to time, memory, and meaning, fostering a deeper understanding of the conditions that govern contemporary media environments. They expand understanding of an evolving relationship between storytelling, computation, and human perception. The approach challenges the fixity of meaning, presenting a dynamic and ever-shifting expressions that reflect the instabilities of memory, time and experience.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.568

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.425
Teacher spread0.382 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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