<i>Well</i> weird, <i>right</i> dodgy, <i>very</i> strange, <i>really</i> cool: Layering and recycling in English intensifiers
Bibliographic record
Abstract
This article examines variable usage of intensifiers in a corpus from a socially and generationally stratified community. Using multivariate analyses, the authors assess the direction of effect, significance, and relative importance of conditioning factors in apparent time. Of 4,019 adjectival heads, 24% were intensified, and there is an increase in intensification across generations. Earlier forms (e.g. right and well) do not fade away but coexist with newer items. The most frequent intensifiers, however, are shifting rapidly. Very is most common, but only among the older speakers. In contrast, really increases dramatically among the youngest generation; however, the effects of education and sex must be disentangled. The results confirm that variation in intensifier use is a strong indicator of shifting norms and practices in a speech community. Studying such actively changing features can make an important contribution to understanding linguistic change as well as to discovering current trends in contemporary English.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".