Working with Words: Research Approaches to Translation-Oriented Lexicographic Practice1
Bibliographic record
Abstract
Dictionaries ideally should address the needs of particular types of users. Their micro and macrostructural design should be oriented towards what user groups need to know about words and the uses that will be made of such knowledge. One specific use for dictionaries is the activity of translation. From the perspective of professional translators, dictionaries should allow for creativity and dynamicity in text production, providing solutions for changing communication needs. Dictionary-making for such a purpose can and should benefit from insights into words and meanings from other fields. Interdisciplinarity in Lexicography is just one example of how other fields interact in Translation Studies. In this paper we analyse how working in an interdisciplinary way is crucial to developing useful lexicographic and terminographic tools for translators and how methodologies, such as corpus-based work and experimental methods should be combined to offer converging evidence of different aspects of use and processing. We illustrate such work methods with examples from real lexicographic projects.
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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 | 0.047 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".