Think <i>really different</i>: Continuity and specialization in the English dual form adverbs
Bibliographic record
Abstract
This paper analyses variation between ‐ly and ‐ø in English dual form adverbs by examining conversational data from York, U.K. Using multivariate analysis and the comparative method we assess the constraint ranking, significance and relative importance of external factors (age, sex, education level) and internal factors (lexical identity, function and meaning). The results show that ‐ly is dominant and has increased dramatically in apparent time. However, cross‐tabulations with individual lexical items reveal that this correlation with speaker age is restricted to a single item–really. In conjunction with evidence from the history of English, we suggest that this does not reflect ongoing developments in English adverb formation, but is the result of continuous renewal in the encoding of ‘intensity’. In contrast, separate analysis of the other adverbs shows that variation between ‐ly and zero is retained in part as a socio‐symbolic resource, in particular for marking less educated male speech. Underlying this social meaning however, is a linguistic constraint which operates across all speakers. The zero adverb encodes concrete, objective meaning–a tendency which can be traced back 650 years or more. This provides yet another example of the interface between social and historical developments in language variation and 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".