Bibliographic record
Abstract
THE leading account of English-language lexicography has for a decade been a book of about 480 pages, lightly illustrated, which opens with a typology of dictionaries and then proceeds via a diachronic section to a synchronic one. This book, Sidney Landau’s Dictionaries: The Art and Craft of Lexicography (rewritten 2nd edn, 2001; 1st edn, 1984), has now been joined by a new one to which every element of this description applies. This is Henri Béjoint’s The Lexicography of English, of which an earlier and much shorter version was published in 1994 as Tradition and Innovation in Modern English Dictionaries and reissued in 2000 as Modern Lexicography: An Introduction. The striking similarities between Landau’s book and Béjoint’s overlie a fundamental difference of approach. The former was shaped by its author’s extensive practical experience of lexicography: in his discussions of the advantages of the semantic tagging of corpora, or of what managing editors do, or of the protection of trademarks, a consistent quiet personal authority was to be heard. The latter, on the contrary, depends very heavily on its author’s excerption and paraphrase of a wide range of secondary sources—including Landau’s Dictionaries, which is quoted or cited 145 times. The statement on the acknowledgements page that ‘I have drawn freely on the writings of the best linguists and metalexicographers I know … to that extent, the book is a compilation’ is not simply the expression of a modesty-topos.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".