A <i>Cool</i> Comparison: Adjectives of Positive Evaluation in Toronto, Canada and York, England
Bibliographic record
Abstract
This paper examines variation and change in the adjectives used to express “highly positive evaluation” in the varieties of English spoken in Toronto, Canada, and York, England. Building on earlier work on another semantic field, “strangeness,” we analyze over 4800 tokens and thirty-four different types, as in “That’s great” and “She’s awesome.” Our results show both similarities and differences between these two semantic fields. While individual forms in both fields tend to be popular for a long time, many forms fall in and out of favor. In the case of adjectives of highly positive evaluation, the adjectival set is particularly rich. Distributional analysis and statistical modeling of constraints on the major forms and their underlying social and linguistic correlates reveals that these changes are not progressing in parallel across varieties of English. There are robust linguistic patterns that suggest a systemic underlying explanation. New additions to this field arise in predicative position and as stand-alones, and in a later stage extend to attributive position. Finally, consistent with earlier findings on adjectives and (intensifying) adverbs, there are notable links to social trends and popular culture, affirming the link between open class categories and their sociolinguistic embedding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".