Routledge Encyclopedia of Translation Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it