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Record W2342715588

Scott Pilgrim vs. the Multimodal Mash-up: Film as Participatory Narrative

2015· article· en· W2342715588 on OpenAlexaboutno aff
Amy C. Chambers, RL Skains

Bibliographic record

VenueBangor University Research Portal (Bangor University) · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeComicsAppropriationCitizen journalismSemioticsParallelsPilgrimAestheticsArtWrightParticipatory cultureMedia studiesSociologyVisual artsLiteratureHistoryArt historyComputer scienceLinguisticsPhilosophyEngineeringWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

This paper examines Scot t Pilgrim vs. The World (Wright, 2010) as a multimodal text, exploring the ways in which the film�s appropriation of aesthetic, semiotic, and narrative tropes from graphic novels and early graphic videogames invites the audience to participate in the narra tive, even while it is delivered through the physically passive, deinteractivating medium of film. Intertextual references to the popular culture of the Gen X era (1980s/90s) abound, evoking emotional responses from a generation that formed, in part, aroun d 8 - bit videogames and comics. The graphic images trigger a participatory engagement through the parallels with the highly interactive medium of videogames, and again forms a nostalgic connection with the audience. In combining media genres and communicati ng through these references to more participatory media, the film�s alternate Toronto becomes more than a secondary world; it becomes a virtual world created in part by the audience�s cognitive participation.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.011
Scholarly communication0.0070.004
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.145
GPT teacher head0.305
Teacher spread0.159 · 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 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
Published2015
Admission routes1
Has abstractyes

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