Bibliographic record
Abstract
Within months of the publication of the final part of the first edition of Skeat’s Etymological dictionary of the English language, the compiler of the leading English etymological dictionary of the 1860s and 1870s, Hensleigh Wedgwood, published a volume of animadversions: Contested etymologies in the dictionary of the Rev. W.W. Skeat (1882). In this paper, I examine Wedgwood’s Contested etymologies, with particular attention to its treatment of non-linguistic historical information; its common-sense arguments about semantic development; its attitude to reconstructed forms; its use of lexical material from non-standard language varieties; its comparativistic breadth; and its relationship to the great tradition of nineteenth-century comparative philology. Skeat’s dictionary superseded Wedgwood’s, and to that extent, the Contested etymologies were the last protest of an old school of etymological scholarship against the work of a new age. But I argue that the gulf between Wedgwood and Skeat should not be exaggerated. Skeat emended fifty of the two hundred entries on which Wedgwood commented to take some account of the older scholar’s criticisms, and even today, Wedgwood’s Contested etymologies, like his dictionary, still has stimulating material to offer its readers.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".