Mutual Illumination: The <i>Dictionary of Old English</i> and the Ongoing Revision of the <i>Oxford English Dictionary</i> (OED3)
Bibliographic record
Abstract
In a volume of papers on the Dictionary of Old English (DOE), it will not seem out of place for the perspective of the new edition of the Oxford English Dictionary(OED3) to be represented, as both are major current projects in the historical lexicography of English which are now progressing simultaneously and often in close collaboration. The aim of this article is to illustrate how ongoing work on the DOE informs and complements work on OED3 and vice versa. Once both works have covered the whole range of the alphabet, together they will, on the one hand, make it possible to obtain a more up-to-date and comprehensive account of the Old English lexicon than was previously available and, on the other hand, relate that lexicon to the overall development of the English vocabulary from its pre-history to the present day, while the etymological coverage in OED3 will help to place it even more comprehensively in context. As the online preface states, the ultimate aim of DOE is to complement “the Middle English Dictionary (which covers the period C.E. 1100-1500) and the Oxford English Dictionary, the three together providing a full description of the vocabulary of English.”
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".