Register, Source Language, and Cognateness Effects on Lexical Choice in Translated Dutch
Bibliographic record
Abstract
In line with recent studies about register and source language effects on translated language (Delaere, De Sutter, et al. 2012; De Sutter, Delaere, et al. 2012; Kruger and Van Rooy 2012; Delaere and De Sutter 2017), the aim of this paper is twofold: to further investigate the influence of register and source language, and to study any potential influence of the variable “cognateness” on translated language. We focus on specific onomasiological choices (lexical choices) in the semantic field of inchoativity, made by translators into Dutch and attested in corpus observations (Dutch translated texts in the Dutch Parallel Corpus). First, we performed a multinomial regression analysis on our dataset and carried out an Analysis of Deviance to determine whether the predictor variables “source language lexeme” and “register” (“text type”) have a significant influence on the response variable (the set of lexemes representing the onomasiological choice range in translated Dutch inchoativity). Doing a second multinomial regression analysis, followed by an Analysis of Deviance, we investigate the influence of the new variable “cognateness” on the translator’s onomasiological choice (in the target language). Classification trees were generated as statistics-based visualizations of onomasiological choice in translated Dutch (translated from French and translated from English) within the semantic field of inchoativity. The results of the statistical analyses show that register, source language, and cognateness significantly influence the specific lexical choices made by translators. In addition, the visualizations show how the onomasiological choice for some target lexemes can be predicted on the basis of a single source language lexeme, while other choices are more complex, and will also be determined by the register of the text.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".