Evidence of Parallel Processing During Translation
Bibliographic record
Abstract
Three eye tracking experiments test the hypothesis that translation involves parallel rather than sequential processing of the source and target texts. In Experiment 1, a group of professional translators translated texts from their native language Danish into English. The texts included both segments where the order of verb and subject was congruent between source and target text and segments that were non-congruent. Translators gazed significantly longer at the non-congruent segments of the source text, indicating that the structure of the target text is anticipated during source text reading. Two follow-up experiments on first and second language reading demonstrate that this congruence effect in translation is not the result of the non-congruent Danish segments being inherently more difficult than the congruent ones and that the effect is not a general effect in bilingual reading. We conclude that translation is a parallel process and that literal translation is likely to be a universal initial default strategy in translation. This conclusion is strengthened by the fact that all three experiments were relatively naturalistic, due to the combination of remote eye tracking and mixed-effects regression modeling.
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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 | 0.003 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".