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Record W1972832621 · doi:10.7202/1013950ar

Censorship or Profit? The Manipulation of Dialogue in Dubbed Youth Films

2013· article· en· W1972832621 on OpenAlexvenueno aff
Serenella Zanotti

Bibliographic record

VenueMeta Journal des traducteurs · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsnot available
Fundersnot available
KeywordsCensorshipDepictionHuman sexualityProfit (economics)AdvertisingSociologyField (mathematics)Media studiesPsychologyAestheticsCriminologyPublic relationsPolitical scienceLawArtLiteratureGender studiesBusinessEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Youth films are a field of special interest to AVT scholars due to the genre’s propensity for potentially disturbing content, both thematic and linguistic. In youth films the depiction of teenage sexuality, violence, crime and drug consumption often combines with strong language. Both represent problematic areas for the dubbing translators, who often act as censorial agents under the pressure of a number of factors, including target cultural norms and values as well as local regulations. Existing work on censorship in AVT has showed that potentially disturbing elements tend to be toned down, if not censored, in dubbed films. Based on both textual analysis and archival research, this study provides further evidence to previous research, while also showing the role of distribution companies in deciding the level of manipulation imposed on the film dialogue.

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)0.004
metaresearch head score (Gemma)0.017
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: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.011
Scholarly communication0.0110.006
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.154
GPT teacher head0.269
Teacher spread0.115 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

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