Bibliographic record
Abstract
English adjective comparison presents a textbook case for variation analysis, exhibiting robust variation between synthetic ( angrier/ angriest) and analytic ( more/most angry) forms. However, our understanding of this alternation relies largely on evidence from written genres; very little is known about adjective comparison in speech, particularly in the vernacular. Since it is likely that analytic comparison gained traction via analogy with French, the shortage of spoken evidence proves a critical gap in our knowledge. This article examines comparison strategies in a large corpus of spoken New Zealand English (NZE), and compares the results with those from another colonial variety, Canadian English. Consistent with reports elsewhere, synthetic comparison is the preferred overall strategy. However, the analysis reveals a system that is partitioned: comparison is not particularly variable in these materials, either synchronically or diachronically. Individual adjectives tend to pattern either synthetically or analytically, raising questions about an across-the-board functional overlap between comparative forms. This article explores a number of explanations (e.g., variation is register-specific or variety-specific, or may be visible only in extremely large corpora), and ultimately suggests that in contrast to the situation found in written and expository genres more generally, variation is limited in vernacular speech.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".