Where did<i>wer</i>go? Lexical variation and change in third-person male adult noun referents in Old and Middle English
Bibliographic record
Abstract
Abstract The present study uses variationist quantitative methods to examine the evolution of the semantic field of third-person male adult noun referents from Old English to Middle English, covering a time depth of approximately six hundred years. Results show a shift from the favored variantwerin Old English tomanin Middle English, with the diachronic change in frequency following a prototypical s-shaped distribution. Although the replacement seems to take centuries to be complete, lexical frequency and written transmission are proposed as influential explanatory factors, and a homonymic clash is suggested to have accelerated the process of replacement in Middle English. Text type and text origin contribute to variation, with alliteration significantly influencing lexical choices in Old English verse texts. When combined with findings from recent synchronic work, this study highlights a heterogeneously structured semantic domain, which has undergone lexical replacement and change over time, providing some evidence for the applicability of s-shaped patterns for lexical change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".