Applying Corpus Data to Define Needs in Web Localization Training
Bibliographic record
Abstract
Localization is increasingly making its way into translation training programs at university level. However, there is still a scarce amount of empirical research addressing issues such as defining localization in relation to translation, what localization competence entails or how to best incorporate intercultural differences between digital genres, text types and conventions, among other aspects. In this paper, we propose a foundation for the study of localization competence based upon previous research on translation competence. This project was developed following an empirical corpus-based contrastive study of student translations ( learner corpus ), combined with data from a comparable corpus made up of an original Spanish corpus and a Spanish localized corpus. The objective of the study is to identify differences in production between digital texts localized by students and professionals on the one hand, and original texts on the other. This contrastive study allows us to gain insight into how localization competence interrelates with the superordinate concept of translation competence, thus shedding light on which aspects need to be addressed during localization training in university translation programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".