Assessing indicators of cognitive effort in professional translators: A study on language dominance and directionality
Bibliographic record
Abstract
Recently in translation studies, important advances have been made with respect to directionality (i.e., whether translation is done into one's native or non-native language). What was once considered the "elephant in the room," directionality now has a growing number of empirical studies that analyze factors which contribute differentially to translation. In this chapter, we review variables that have been previously identified as related to a higher or a lower degree of cognitive activity in direct and inverse translation (DT and IT, respectively). Against this backdrop, we present a study conducted among professional translators of English and Spanish who completed two translation tasks: one in which they translated a text from English into Spanish and another in which they translated another text from Spanish into English. We use behavioral and eye-tracking measures to analyze time, mouse events, keypresses, saccade index, and gaze index data. We also explore the effects of age and sex/gender. The results suggest that in terms of length, although translators spent longer in IT compared to DT, this difference was not statistically significant. However, there was a correlation between translation direction and fixation index such that participants showed a higher gaze event duration in IT. Age was correlated to fixation index (lower fixation index among older translators) and sex/gender was also related to fixation index (females presented lower values in IT in comparison to DT). Results also suggested a higher gaze point index in IT and a higher keypress index for English-dominant translators, and a higher gaze point index in DT for Spanish-dominant translators. Overall, our study suggests that although some of the variability in the results is likely due to individual differences, the observed patterns help us better understand differences between DT and IT.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".