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Record W3172313731 · doi:10.7202/1077410ar

Translating planting and payoff in Edgar Wright’s Cornetto trilogy

2021· article· en· W3172313731 on OpenAlexvenueno aff
Taniya Gupta

Bibliographic record

VenueMeta Journal des traducteurs · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsTrilogyWrightNarrativeOrder (exchange)Shot (pellet)Plot (graphics)Hindsight biasJournalismMedia studiesSociologyArtHistoryLiteraturePsychologyArt historySocial psychology

Abstract

fetched live from OpenAlex

“Planting and payoff” is a narrative technique in cinema where future plot events are foreshadowed by means of a verbal or visual hint that later acquires greater significance in hindsight. This article examines the use of this technique in British director Edgar Wright’s Cornetto trilogy (Shaun of the Dead, Hot Fuzz and The World’s End) in order to discover if these plants and payoffs are accessible to a Spanish speaking audience in the subtitled and dubbed DVD versions. Edgar Wright is known for his extensive use of verbal-visual foreshadowing and there are multiple Internet forums, film websites and vlogs by film students and fans that discuss the secrets waiting to be found by observant viewers. Using Chaume’s (2004a, 2012) list of signifying codes, this article attempts to isolate relevant frames of certain shots and, by breaking down the verbal and nonverbal information coded in each shot, it examines whether this foreshadowing is communicated in the subtitled and dubbed versions of these films. In doing so, it explores how information is shared between the different components of film language and the importance of taking into consideration the role of nonverbal codes in audiovisual translation.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.003
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 head0.096
GPT teacher head0.276
Teacher spread0.180 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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