Routledge Encyclopedia of Translation Technology
Bibliographic record
Abstract
Introduction Chan Sin-wai Acknowledgement Part 1: General Issues of Translation Technology * The Development of Translation Technology: 1967-2013 Chan Sin-wai * Computer-aided Translation: Major Concepts Chan Sin-wai * Computer-aided Translation Systems Ignacio Garcia * Computer-Aided Translation: Translator Training Lynne Bowker * Machine Translation: General Liu Qun and Zhang Xiaojun * Machine Translation: History of Research and Applications W. John Hutchins * Example-based Machine Translation Billy Wong Tak-ming and Jonathan Webster * Open-Source Machine Translation Technology Mikel L. Forcada * Pragmatics-based Machine Translation David Farwell and Stephen Helmreich * Rule-based Machine Translation Yu Shiwen and Bai Xiaojing * Statistical Machine Translation Liu Yang and Zhang Min * Evaluation in Machine Translation and Computer-aided Translaton Kit Chunyu and Wong Tak-ming * The Teaching of Machine Translation: The Chinese University of Hong Kong as a Case Study Cecilia Wong Suk Man Part 2: The National / Regional Developments of Translation Technology * Translation Technology in China Qian Duoxiu * Translation Technology in Canada Elliott Macklovitch * Translation Technology in France Sylviane Cardey * Translation Technology in Hong Kong Chan Sin-wai, Ian Chow and Wong Tak-ming * Translation Technology in Japan Hitoshi Isahara * Translation Technology in South Africa Gerhard van Huyssteen and Marissa Griesel * Translation Technology in Taiwan: Track and Trend Shih Chung-ling * Translation Technology in the Netherlands and Belgium Leonoor van der Beek and Antal van den Bosch * Translation Technology in the United Kingdom Christophe Declercq * A History of Translation Technology in the United States of America Jost Zetzsche and Jennifer DeCamp Part 3: Specific Topics in Translation Technology * Alignment Lars Ahrenberg * Bitext Alan K. Melby, Arle Lommel, and Lucia Morado Vazquez * Computational Lexicography Zhang Yihua * Concordancing Federico Zanettin * Controlled Language Rolf Schwitter * Corpus Li Lan * Editing in Translation Technology Christophe Declercq * Information Retrieval and Text Mining Kit Chunyu and Nie Jian-Yun * Language Codes and Language Tags Sue Ellen Wright * Localization Keiran Dunne * Natural Language Processing Olivia Kwong * Online Translation Federico Gaspari * Part of Speech Tagging Felipe Sanchez-Martinez * Segmentation Freddy Y. Y. Choi * Speech Translation Tan Lee * Subtitling and Technology Jorge Dias-Cintas * Terminology Management Kara Warburton * Translation Memory Alan K. Melby and Sue Ellen Wright * Translation Management Systems Mark Shuttlewort
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.303 | 0.222 |
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".