Bibliographic record
Abstract
Recent years have witnessed a significant growth of corpus-based translation studies that appeared in the beginning of the 1990s. Corpus linguistics has provided a new weapon for translation studies, broadened the research scope and introduced a brand-new thought pattern for translation scholars. This paper introduces the design and application of Translational English Corpus. Besides, it makes an objective assessment to corpus-based translation studies and analyses the potential of Translational English Corpus. Key words: Corpus; Corpus linguistics; Translation studies; Advantages; Limitations Resume: Ces dernieres annees ont connu une croissance importante des etudes de traduction a base de corpus qui est apparue au debut des annees 1990. La linguistique de corpus a fourni une nouvelle arme pour les etudes de traduction, elargi le champ de recherches et introduit un mode de pensee tout nouveau pour les specialistes de la traduction. Cet article presente la conception et l'application de corpus translationnel en anglais. En outre, il fait une evaluation objective sur des etudes de traduction a base de corpus et analyse le potentiel de corpus translationnel en anglais.Mots-cles: corpus; linguistique de corpus; etudes translationnelles; avantages; limitations
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".