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Record W2695793857 · doi:10.1075/tis.12.2.06dmi

Translator training in Canada and Russia

2017· article· en· W2695793857 on OpenAlexaffabout
Gleb Dmitrienko

Bibliographic record

VenueTranslation and Interpreting Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsInterpretation (philosophy)Translation studiesInstitutionalisationField (mathematics)Multidisciplinary approachVariety (cybernetics)Training (meteorology)SociologyPerceptionEngineering ethicsEpistemologyComputer sciencePolitical scienceSocial scienceArtificial intelligenceLinguisticsEngineeringLaw

Abstract

fetched live from OpenAlex

Abstract The purpose of this paper is to shed some light onto contemporary translation didactics as a “virgin” field of applied TS that cannot be successfully developed without a new, multidisciplinary approach that would put forward the specificity of translator training as a distinct, socially valuable practice. We hypothesize that as socially-specific, practice-oriented products of the interaction between the systems of translation and of professional education, translator training programs are dependent on the social perception of translating activity as well as on the degree of its institutionalization as a profession. Given that contemporary translation and interpretation practices, as well as translator training programs, are limited to local manifestations, the social and cultural discrepancies impede any comparativism in this field of applied TS. However, in applying a sociological approach to translator training, we propose a methodological framework for a sociologically-informed comparative analysis that would lift the cultural and institutional barriers that until now have been distorting our vision of translation as a global social practice and have thus prevented us from conducting comparative analysis of a wide variety of translational phenomena as manifested in different locales, conceived in terms of both time and space. In order to illustrate our propositions, we present the reader with a case study of the most prototypical translator training programs in Canada and Russia – countries that, due to the differences in the theoretical, practical and didactic setup of their respective fields of translation and interpretation, offer appropriate support for our comparative methodology.

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 score0.702
Threshold uncertainty score0.673

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.122
GPT teacher head0.319
Teacher spread0.197 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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