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Record W2989786312 · doi:10.7202/1065569ar

Revisiting “Speak White”: A lieu de mémoire Lost and Found in Translation

2019· article· en· W2989786312 on OpenAlexaffvenueabout
Carmen Ruschiensky

Bibliographic record

VenueTTR traduction terminologie rédaction · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsConcordia University
Fundersnot available
KeywordsPoetryWhite (mutation)Context (archaeology)LinguisticsRewritingForegroundingGenerative grammarIncarnationMateriality (auditing)Transformative learningLiteratureHistoryArtSociologyAestheticsPhilosophyComputer scienceTheology

Abstract

fetched live from OpenAlex

This article traces the afterlives of Michèle Lalonde’s 1968 poem “Speak White” to explore how translation contributes to constructing, renewing and transforming it as a lieu de mémoire through various transformative processes. The term “translation” here designates a phenomenon that includes but extends beyond the concept of translation as linguistic transfer to encompass different forms of rewriting, adaptation and remediation, foregrounding the generative aspect of the memory site as well as the tension between past and present, between a lieu de départ and its reinscription in a new context. Specifically, it focuses on two English translations of “Speak White” that attempt to reconstruct the poem’s subversive diglossia; Marco Micone’s 1989 poem “Speak What,” as a rewriting that takes the form of serious parody; two adaptations produced during the 2012 Quebec Student Strike, “Speak Red” and “Speak rich en tabarnaque”; and the latest incarnation of “Speak White” in Robert Lepage’s 887 , a theatrical production that introduces its own layers of intertemporal, intermedial and interlingual complexity. These recreations of “Speak White” reveal how a lieu de mémoire can be simultaneously anchored or re-anchored in the past while also being renewed or rerouted through translation in the present across languages, cultures, media and time.

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 score0.923
Threshold uncertainty score0.592

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 head0.085
GPT teacher head0.294
Teacher spread0.210 · 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

Citations5
Published2019
Admission routes3
Has abstractyes

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