MétaCan
Menu
Back to cohort
Record W3144370998 · doi:10.7202/1075844ar

Reduced abstractness in Spanish-English translation: the case of property-denoting nouns

2021· article· en· W3144370998 on OpenAlexvenueno aff
Anna Espunya

Bibliographic record

VenueMeta Journal des traducteurs · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
FundersMinisterio de Economía y Competitividad
KeywordsNounLinguisticsComputer scienceAdjectiveNatural language processingArtificial intelligencePsychologyPhilosophy

Abstract

fetched live from OpenAlex

This study proposes an empirical methodology to test the hypothesis that translation from Spanish into English results in reduced abstractness. The semantic area focused upon is the attribution of properties to specific entities by means of abstract nouns. Two levels of abstractness, conceptual and grammatical, are considered. Conceptual abstractness is linked to the referential content of the nouns, while grammatical abstractness involves the reified expression of properties as nouns, as opposed to other word classes. The study classifies the translation correspondences for nouns ending in the suffix -idad in the Spanish novels by Manuel Vázquez Montalbán (1939-2003) Los mares del Sur and Tatuaje . Such property-denoting nouns contribute to the construal of point of view. The methodology combines a quantitative approach with a qualitative, text-analytic selection of relevant items. Results indicate that abstractness is mainly reduced on the grammatical level although instances of diminished conceptual abstractness can also be observed. Compliance with language preferences may be aligning with readability norms affecting the target product, crime fiction, to undo reification of properties (namely explicitation and simplification). The results underscore the need to include semantic parameters in studies of translation tendencies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.117
GPT teacher head0.290
Teacher spread0.173 · 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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueMeta Journal des traducteursSame topicTranslation Studies and PracticesFrench-language works237,207