On being happier but not more happy: Comparative alternation in speech data
Bibliographic record
Abstract
English adjective comparison is increasingly the focus of corpus linguistic research, but it is much less studied in the variationist framework. These two traditions converge, however, in revealing robust variation between historical inflection ( happier/happiest ) and newer periphrasis ( more/most happy ). However, our understanding of the strategies for comparison comes from written genres. In contrast, very little is known about comparison in vernacular speech. Since periphrastic comparison emerged as a change from above, the lack of spoken evidence proves a critical gap in our knowledge. To address this gap, this paper examines comparison strategies in New Zealand English, drawing on the whole of the Origins of New Zealand English Archive (Gordon et al. 2007). Analysis of 1400 tokens reveals a striking result. Consistent with reports elsewhere, inflection is the preferred mode of comparison. However, consideration by lexical item reveals a system that is not, in fact, variable. Rather, across the history of this variety (speakers born 1851-1982), individual adjectives pattern one way (inflection) or the other (periphrasis); in speech, the form of comparison has consistently been lexically conditioned, and by extension, invariant. This paper explores a number of explanations (e.g. variation is genre-specific or variety-specific, or may only be visible in extremely large corpora), and ultimately concludes that in speech, historical variation resulted in the full ‘regularization of a confused situation’ (Bauer 1994:60).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".