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Record W2891506565 · doi:10.7202/1051015ar

Underground Games: Surface Translation and the Grotesque

2018· article· en· W2891506565 on OpenAlexaffvenue
Ryan Fraser

Bibliographic record

VenueTTR traduction terminologie rédaction · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
Fundersnot available
KeywordsAmbivalenceStyle (visual arts)Translation studiesConversationComicsAestheticsProduct (mathematics)LiteratureEpistemologySociologyArtLinguisticsPhilosophyPsychologyPsychoanalysisMathematicsGeometry

Abstract

fetched live from OpenAlex

Referenced by theory for seemingly contradictory purposes, the practice of “surface translation” has an ambivalent status within Translation Studies. This is not surprising, as the principle of ambivalence informs both its composition and its conversation with its reader. Nevertheless, a positive step toward a more productive conception of surface translation was accomplished by Jean-Jacques Lecercle (1990), who defined it as a form in extremis of linguistic interference or mixing. Guided by this conception, I would argue here that the practice is in all respects identifiable with the Classical and Medieval ornamental style known by art history as the “grotesque.” This is the first study to identify surface translation with the grotesque. Five specific points of comparison are leveraged here: 1) Both surface translation and grotesque art are created through the proscribed mixing of incompatible materials; 2) Both are peripheral art forms involving play with margins; 3) Both aspire toward the “perverse,” “comic,” and/or “monstrous” in their mixes; 4) Both tend to be explained as the product of impulsive thinking; 5) The experience that these mixtures are designed to produce is “ambivalence.”

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.566

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 studies0.0010.001
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 head0.099
GPT teacher head0.289
Teacher spread0.190 · 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 designNot applicable
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
Published2018
Admission routes2
Has abstractyes

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