Reduced abstractness in Spanish-English translation: the case of property-denoting nouns
Bibliographic record
Abstract
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 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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".