A Weird (LANGUAGE) Tale: Variation and Change in the Adjectives of Strangeness
Bibliographic record
Abstract
This article presents a synchronic quantitative study of adjectives in the semantic field of strangeness in a large North American city, Toronto, the largest urban center in Canada. The analysis is based on nearly 2,000 adjectives, representing 11 different types, as in She’s really weird and She’s odd. The distribution of these adjectives in apparent time provides startling evidence of change. The adjective strange is quickly moving out of favor, and weird has expanded dramatically, usurping all other forms. Neither linguistic nor social factors are implicated in this change, suggesting that lexical replacement is the prevailing mechanism driving the development. Consideration of the broader context reveals that renewal and recycling of these adjectives is rooted in the history of English and is progressing in parallel at least across British and North American English. The actuation of the shift toward weird may be rooted in developments in literature and mass media, revealing that adjectives are a vibrant area of the grammar that may be used to track cultural influences on linguistic change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".