Pedro Fuertes-Olivera, and Sven Tarp. Theory and Practice of Specialised Online Dictionaries. Lexicography and Terminography
Bibliographic record
Abstract
This book presents a theoretical and methodological approach to specialized lexicography defined by the authors as ‘the branch of lexicography concerned with the theory and practice of specialised dictionaries, i.e. dictionaries, encyclopaedias, lexica, glossaries, vocabularies, and other information tools covering areas outside general and cultural knowledge and the corresponding Language for General Purposes (LGP); it represents mainly, but not exclusively, disciplines related to technology, industry, trade, economic life, law, natural and social sciences, and humanities.’ (p. 7) The focus has been placed on electronic specialized lexicography and online resources but part of the ideas and principles presented can be applied to printed media. The approach taken is deeply rooted in the function theory of lexicography that will be briefly described below. The first author, Pedro Fuertes-Olivera, professor at the University of Valladolid (Spain), has applied the function theory to specialized lexicographical projects and is the author or editor of other books on specialized lexicography (Fuertes-Olivera 2010; Fuertes-Olivera and Arribas-Baño 2008); the second author, Sven Tarp, professor at the University of Aarhus (Denmark), is also a lexicographer and is the main advocate of the function theory (Tarp 2008).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".