Bibliographic record
Abstract
This textbook provides an accessible introduction to the field of translation for students of other disciplines and readers who are not translators. It provides students outside the translation profession with a greater awareness of, and appreciation for, what goes into translation. Providing readers with tools for their own personal translation-related needs, this book encourages an ethical approach to translation and offers an insight into translation as a possible career. This textbook covers foundational concepts; key figures, groups, and events; tools and resources for non-professional translation tasks; and the types of translation that non-translators are liable to encounter. Each chapter includes practical activities, annotated further reading, and summaries of key points suitable for use in classrooms, online teaching, or self-study. There is also a glossary of key terms. De-mystifying Translation: Introducing Translation to Non-translators is the ideal text for any non-specialist taking a course on translation and for anyone interested in learning more about the field of translation and translation studies. The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under a Creative Commons (CC BY-NC-SA) license.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.018 |
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".