Between Homonymy and Polysemy: The Origins and Career of the English Form Dialect in the Sixteenth Century
Bibliographic record
Abstract
Abstract The emergence of the form dialect in early modern English is often mentioned in histories of the language, but important as it is, the evidence for it has never been analyzed as a whole, and its treatment in the revised OED entry for dialect leaves room for modifications. This article presents and re-evaluates the evidence for dialect in sixteenth-century English sources. It demonstrates that there were two homonyms with this form, one a shortening of English dialectics and one a borrowing from post-classical Latin dialectus, from its Greek etymon διάλεκτος, and, less often, from French dialecte. After treating dialect ‘dialectics’ briefly, it explores the known attestations of dialect ‘kind of language’, showing the range of senses in which this word could be used, and the ways in which it can be shown to have spread from one user of English to another, beginning with one clearly defined expatriate learned circle in the 1560s, entering more general learned use in the 1570s and 1580s, and becoming a fully naturalized literary English word in the 1590s. The paper therefore offers a detailed case-study of the naturalization of a learned word in early modern English and also contributes to the history of the conceptualization of language variation in sixteenth-century England.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".