Bibliographic record
Abstract
The aim of this volume is to record the resurgent influence of Language Learning in Translation Studies and the various contemporary ways in which translation is used in the fields of Language Teaching and Assessment. It examines the possibilities and limitations of the interplay between the two disciplines in attempting to investigate the degree to which recent calls for reinstating translation in language learning have borne fruit. The volume accommodates high-quality original submissions that address a variety of issues from a theoretical as well as an empirical point of view. The chapters of the volume raise important questions and demonstrate the beginning of a new era of conscious epistemological traffic between the two aforementioned disciplines. The contributors to the volume are academics, researchers and professionals in the fields of Translation Studies and Language Teaching and Assessment from various countries and educational contexts, including the USA, Canada, Taiwan R.O.C., and European countries such as Belgium, Germany, Greece, Slovenia and Sweden, and various professional and instructional settings, such as school sector and graduate, undergraduate and certificate programs. The contributions approach the interplay between the two disciplines from various angles, including functional approaches to translation, contemporary types of translation, and the discursive interaction between teachers and students.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".