Women’s Words and the Words of Women in the <i>Oxford English Dictionary</i>
Bibliographic record
Abstract
Abstract This article explores datasets curated from the citation evidence in successive editions and revisions of the Oxford English Dictionary (1884–2022), which have been annotated to reflect the gender of the authors and other bibliographical metadata. This exploration aims both to supplement the historical account of the dictionary’s uses of female-authored quotation sources, correcting and elaborating some figures which have previously been reported, and to provide a contemporary account of women’s representation in OED Online, using the revision published in June 2022. In seeking to establish a more objective and empirical basis for judging ‘representativeness’, I treat the OED both as a self-contained bibliographical and lexicographical work, and comparatively, against other comprehensive or very large bibliographical corpora, namely the Garside et al. surveys of early English novels, the Library of Congress Catalog, and the HathiTrust Digital Library. The OED data studied here represents a significant (if restricted) subset, rather than a representative sample, of the OED corpus as a whole: modern (post-1700) quotations from books appearing with their author’s name in the OED evidence are considered. While this approach does not claim to make an objectively complete tally of every woman-authored quotation collected in the OED, it does enable a more detailed and accurate account than has previously been possible, and allows for a number of consistent cross-comparisons. A companion document of Supplementary Data & Notes, available at The Review of English Studies online, describes in technical terms how the data was compiled and the processes and principles by which it was annotated.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".