LYNDA MUGGLESTONE, Lost for Words: the Hidden History of the Oxford English Dictionary. Pp. xxv+273. New Haven and London: Yale University Press, 2005. 19.95 (ISBN 0 300 10699 8).
Bibliographic record
Abstract
THE history of the making of The Oxford English Dictionary, like the dictionary itself, is an immense subject inviting a multitude of perspectives and approaches. In Lost for Words, Lynda Mugglestone has, through original archival research, skilfully exposed the processes by which the first edition of OED (originally titled A New English Dictionary on Historical Principles) was generated. One feature of Mugglestone's book that may invite a broader readership than it would otherwise have received is its inclusion of a quite readable history of the writing of OED, from its initial conception by Richard Trench and the Philological Society to the many events and circumstances – including inefficient editors, financial difficulties, personal squabbles, and national calamities, some of which threatened to shut the project down altogether – to its triumphant completion in 1928. This important contextual work, though not new territory per se, is necessary to Mugglestone's main argument because it shows the external pressures that Murray and his colleagues faced, in particular the repeated demand by the delegates of the press to pare down each fascicle of the dictionary, a pressure which influenced many of the decisions discussed throughout the book.
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 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.016 |
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".