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Record W2114649979 · doi:10.7202/1023817ar

A Corpus-based Approach to the Compilation, Analysis, and Translation of Rural Tourism Terms

2014· article· en· W2114649979 on OpenAlex
María Isabel Fijo León, Adrián Fuentes-Luque

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMeta Journal des traducteurs · 2014
Typearticle
Languageen
FieldArts and Humanities
Topiclinguistics and terminology studies
Canadian institutionsnot available
Fundersnot available
KeywordsTourismAccommodationTranslation (biology)Rural tourismComputer scienceLinguisticsNatural language processingParallel corporaArtificial intelligenceMachine translationPolitical scienceTourism geographyPsychology

Abstract

fetched live from OpenAlex

This paper describes an empirical, corpus-based analysis of the terms related to rural tourism accommodation establishments in three languages – Spanish, English, and French. The main objective is to identify and describe common characteristics shared by the concepts and the terms designating them in these languages, with the aim of suggesting the most suitable translation equivalents. The study is based on a computerized corpus divided into three comparable subcorpora. The results obtained from the analysis have direct implications for the translation of rural tourism texts.

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.

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 score0.667
Threshold uncertainty score0.331

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 communication0.0000.000
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 head0.058
GPT teacher head0.249
Teacher spread0.191 · 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