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Record W4242469287 · doi:10.1075/z.142

Textual Translation and Live Translation

2008· book· en· W4242469287 on OpenAlexaff
Fernando Poyatos

Bibliographic record

VenueJohn Benjamins Publishing Company eBooks · 2008
Typebook
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsParalanguageLinguisticsFluencyReading (process)Movie theaterPerceptionNonverbal communicationKinesicsPsychologyCommunicationArtLiterature

Abstract

fetched live from OpenAlex

After the many interdisciplinary perspectives on nonverbal communication offered by the author in his previous seven John Benjamins books, which have generated a wide range of scholarly applications, the present monograph is dominated by a very broad concept of translation. This treatment of translation includes theater and cinema (enriching our intellectual-sensorial experience of both 'reading act' and 'viewing act') and offers among other topics: sensorial-intellectual-emotional pre- and post-reading interactions with books; mute or audible 'oralization' of texts; the translator's linguistic and nonverbal-cultural fluency and implicit textual paralanguage and kinesics; translating functions of pictorial illustrations; the blind's text and film perception; the foreign reader's cultural background and circumstances; theater and cinema spectators' total sensory-intellectual experience of plays and films beyond staging or projection; the multiple interrelationships between cinema and theater performers, spectators and their environments, of special interest to all those involved in the theater; and the translator's challenging textual perception of sounds and movements. Over 800 literary quotations, and two virtually exhaustive English inventories of sound- and movement-denoting words with many examples, offer serious students of translation, language or literature a rich reference and drill source.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.875
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
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 head0.103
GPT teacher head0.255
Teacher spread0.152 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations10
Published2008
Admission routes1
Has abstractyes

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