Structural explanations in syntactic variation: The evolution of English negative and polarity indefinites
Bibliographic record
Abstract
Abstract It is well documented that the study of differences in grammaticality contrasts across the world's languages has implications for the synchronic study of preferential/frequency contrasts within a single language. Our paper extends this observation, arguing that the cross-linguistic study of both grammaticality and frequency contrasts can be crucial to the proper characterization of patterns of diachronic change. As an illustration of this proposal, we investigate patterns of synchronic and diachronic variation in the use of postverbal negative quantifiers (e.g., nothing, nobody, no book, etc., as in, I know nothing ) versus negative polarity items under negation (e.g., not … anything, not … anybody, not … any book , etc., as in, I do n't know anything ) in English. We show how a detailed comparison with similar patterns found elsewhere in closely related languages can give us a better understanding of which linguistic factors condition the use of these different kinds of indefinites in Modern Spoken English and a new perspective on a well-studied proposed change in progress in the English quantificational system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".