The Shifting of the Demonstrative Determiner in French and Dutch in Parallel Corpora: From Translation Mechanisms to Structural Differences
Bibliographic record
Abstract
This paper focuses on translational shifts with respect to the demonstrative determiner in French and Dutch in parallel corpora. The paper aims to identify the types of translation shifts that occur systematically, and to explore the underlying mechanisms and semantic effects of this process. For this purpose, a well-balanced sub-corpus of the Dutch Parallel Corpus is used, making it possible to analyze both directions (French – Dutch and Dutch – French). In this corpus, 50% of the demonstrative determiners are translated by a demonstrative in the target text (in both directions). In 20% of the cases, the demonstrative is translated by a definite article, or vice versa, while 30% are translated by another grammatical element (e.g., indefinite determiner, adverb, personal pronoun) or vice versa. The parallel corpus study reveals that translational shifts with respect to French and Dutch demonstratives can be attributed to three different mechanisms: (1) translator preference related to translation universals at the level of the noun phrase (omissions, additions and reformulations of the noun phrase), (2) specific manifestations of translation universals within the noun phrase (syntagmatic and paradigmatic explicitation and implicitation involving demonstrative shifting) and (3) structural divergences between the French and Dutch demonstrative determiner systems (fixed expressions and semantic differences). This analysis demonstrates the usefulness of a detailed parallel corpus study, which clearly distinguishes between changes occurring at different levels, in accounting for divergent translations of the demonstrative determiner in different languages. To this end, several types of explanation drawn from various fields (such as translation studies and contrastive linguistics), must be considered.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".