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Record W2507405032 · doi:10.7202/1036955ar

Translating an Imagetext: Verbal and Visual Self-Representation in Brett Whiteley’s Interior, Lavender Bay (1976)

2016· article· en· W2507405032 on OpenAlexvenueno aff
Margherita Zanoletti

Bibliographic record

VenueTTR traduction terminologie rédaction · 2016
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsDepictionRepresentation (politics)LinguisticsMeaning (existential)Relation (database)Perspective (graphical)AppealVisual languageVisual artsVisual rhetoricKey (lock)TerminologyHistoryArtPsychologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

This paper explores the relationship between the words and images in the drawing Interior, Lavender Bay by the Australian artist Brett Whiteley (1939-1992). This artwork combines the depiction of the artist’s home with a written element composed of the title, date, artist’s monogram, and a brief inscription. By examining Whiteley’s use of words and images in this drawing, the verbal/visual synergy that underpins his language is emphasized as a key aspect of his communicative appeal. The interpretive lens used in order to analyze Interior, Lavender Bay is interlingual translation. Translating Whiteley’s words from English into Italian allows not only to decipher the literal meaning and comprehend the symbolic function of his words, but also to highlight the relation between art and language. From this perspective, drawing on W. J. T. Mitchell’s Picture Theory (1994), the paper aims to discuss the functioning of images and the way in which interlingual translation might bring out latent connections in the source, opening a window on the interdisciplinary encounter between creative processes in the visual art and translation theory and practice.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.349
Teacher spread0.302 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2016
Admission routes1
Has abstractyes

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