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Record W3133574563 · doi:10.1515/psicl-2020-0009

Shifting genres: Rendering bad language in the Polish voice-over of the Canadian drama American Heist

2020· article· en· W3133574563 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePoznań Studies in Contemporary Linguistics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSwearing, Euphemism, Multilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsDramaTabooLinguisticsMeaning (existential)PsychologySociologyLiteratureArtPhilosophy

Abstract

fetched live from OpenAlex

Abstract This article is devoted to the problem of translating bad language in cases when the target audience recipient has direct access to the source text and the impact translational and editorial choices have on the overall meaning of the work. As an illustration of this point, it discusses a voice-over translation, in which case it is common practice to censor vulgarities, mainly by means of under-translating certain phrases, which are considered taboo, or omitting them completely. Such choices are justified for a number of reasons, including protecting vulnerable audiences or reducing semantic density because most taboo words exercise a phatic function. However, in certain cases censoring bad language has an impact on the semiotic make-up of the work and thus, changes the meaning intended by the original author. A good illustration is provided with the analysis of particular scenes from the Canadian drama American Heist .

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)0.001
metaresearch head score (Gemma)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.162
GPT teacher head0.412
Teacher spread0.251 · 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