Nuria Edo Marza. The Specialised Lexicographical Approach: A Step further in Dictionary Making.
Bibliographic record
Abstract
Specialized lexicography (also labelled terminology or terminography, as Nuria Edo Marzá reminds us), has attracted much interest in the past few years. It would take too much space here to list all the books and research articles devoted to the subject, but it is obvious that many scholars and practitioners (among which stands Edo Marzá) believe it differs from standard lexicography in certain respects and deserves to become a separate subject of study. In this book, the author aims to define a ‘new approach’ to specialized lexicography. The approach labelled the Specialized Lexicographical Approach, SLA, is applied to the field of industrial ceramics. Edo Marzá sets herself to explain the various theoretical and methodological aspects that should be taken into account in this new approach. The book by Nuria Edo Marzá is divided into two main sections. The first one is entitled ‘Theoretical framework’ and contains 6 chapters. The second section bears the title ‘How to compile a specialised dictionary’ and contains 3 chapters and a final section with concluding remarks. The book also includes an 8-page bibliography, three different lists (websites, terminological databases, existing dictionaries of ceramics) and a 4-page subject index. In this first section, I will summarize each chapter; in the following one, I will proceed to give my assessment of the contents of the book.
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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 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.027 |
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".