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Record W2883321830 · doi:10.7202/1050519ar

Translators’ Perspectives: The Construction of the Peruvian Indigenous Languages Act in Indigenous Languages

2018· article· en· W2883321830 on OpenAlexvenueno aff
Raquel de Pedro Ricoy, Rosaleen Howard, Luis Andrade Ciudad

Bibliographic record

VenueMeta Journal des traducteurs · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
FundersArts and Humanities Research Council
KeywordsIndigenousSituatedLinguisticsLegislationLegal translationTranslation studiesColonialismState (computer science)Political scienceSociologyComputer scienceLawArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

An urgent need is emerging in contemporary Latin America for the translation of legal texts from the languages of former European colonial powers into the many indigenous languages spoken across the region. This article addresses the issue in relation to the rise of legislation that requires States to uphold the principle of linguistic human rights. It takes as a case study the translation of the Peruvian Indigenous Languages Act (2011) from Spanish into five Amerindian languages, viewed as a postcolonial practice situated at the communicative interface between the State and the country’s indigenous populations. Our specific interest is the strategic behavior of the indigenous translators, as described by themselves, when communicating to their peoples the State norms contained in the Indigenous Languages Act . In order to analyze this behavior, we depart from text-analytical models and favor an approach based on the translators’ perceptions of their role and their rationales for the translation solutions adopted. The analysis combines theoretical strands from translation studies, legal studies and postcolonial studies so as to throw light on the translation of legal discourse from Spanish into the indigenous languages of Peru, as conducted, crucially, by bilingual translators situated on the cultural “inside.”

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.282
Teacher spread0.249 · 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 designQualitative
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

Citations8
Published2018
Admission routes1
Has abstractyes

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