Translators’ Perspectives: The Construction of the Peruvian Indigenous Languages Act in Indigenous Languages
Bibliographic record
Abstract
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 PeruvianIndigenous 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 theIndigenous 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.”
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".