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Record W4405054127 · doi:10.7202/1114778ar

Diamesic, Interlingual, and Intercultural Translation in Oodgeroo Noonuccal’s<i> Stradbroke Dreamtime</i> (1972)

2024· article· en· W4405054127 on OpenAlexvenueno aff
Margherita Zanoletti

Bibliographic record

VenueTTR traduction terminologie rédaction · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeLiteratureLinguisticsHistoryConceptualizationRealmForegroundingScrutinySociologyArtPhilosophy

Abstract

fetched live from OpenAlex

At the peak of her fame as a poet and political activist, the Australian author Oodgeroo Noonuccal (1920-1994, until 1988 known as Kath Walker) published her first complete work of prose, Stradbroke Dreamtime (1972). An important autobiographical narrative written in accessible English, the book comprises 27 stories for children that present two aspects of Oodgeroo’s life: episodes from her childhood on Minjerribah (North Stradbroke Island) and stories from Stradbroke Island and the Tamborine Mountains, as well as stories based on the author’s knowledge of her people and the land. This study adopts a semiotic-translation approach based on an expanded conceptualization of translation that goes beyond the literary sphere and the verbal realm. It argues that Oodgeroo’s writing entails three types of translation: the transformation of oral knowledge into written narrative (diamesic translation); the resemiotization of stories and legends from Aboriginal Australian languages and dialects into English (interlingual translation); and the transmission of her own life experience and culture to children and teenagers of all descent (intercultural translation). These translation processes enabled Oodgeroo to contextualize her own stories by calling upon the ancient stories of her people. The result is a multilayered literary work of rare complexity, worthy of greater scrutiny and more nuanced readings.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.843

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)0.0000.000

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 head0.071
GPT teacher head0.309
Teacher spread0.238 · 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 designOther design
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

Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueTTR traduction terminologie rédactionSame topicDiscourse Analysis in Language StudiesFrench-language works237,207