“He loved his father but next to adored his mother”: <i>Nigh</i> ( <i>ly</i> ), <i>Near</i> , and <i>Next</i> ( <i>To</i> ) as Downtoners
Bibliographic record
Abstract
In Present-Day English, nearly functions as an approximator downtoner meaning ‘almost, all but, virtually,’ as do earlier variants based on the same root— nigh, nighly, near, next ( to)—though more rarely and in more restricted contexts. Nigh functions as an approximator downtoner in Old and Middle English. When near displaces nigh, nigh is retained as a downtoner with lexical adjectives expressing negative semantic prosody. Near is used as a downtoner in later Middle and Early Modern English. However, degree adjunct uses are not well attested, thus pointing to incomplete grammaticalization. During the eighteenth century, the new -ly form ( nearly) takes over the innovative downtoner function and the old form ( near) is retained in the original locative sense, with some remnant downtoner uses. Next ( to) grammaticalizes as a downtoner, but proceeds only to the degree modifier stage and involves a high degree of idiomaticization, thus suggesting incipient grammaticalization. As spatial adverbs, nigh/ near/ next ( to)/ nearly represent one of the well-known sources for the grammaticalization of degree adverbs. However, these forms seem to follow a pathway where the degree modifier use (adjective/participle modifier) precedes the degree adjunct use (verb modifier), contrary to the reverse pathway postulated for other degree adverbs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".