Language Play is Language Variation: Quantitative Evidence and What it Implies About Language Change
Bibliographic record
Abstract
This article argues that language play is intimately related to linguistic variation and change. Using two corpora of online present-day English, we investigate playful conversion of adjectives into abstract nouns (e.g. made of awesome ∅), uncovering consistent rule-governed patterning in the grammatical constraints in spite of this option stemming from deliberate subversion of standard overt suffixation. Building on Haspelmath's (1999) notion of ‘extravagance’ as one of the keys to language change, we account for the systematic patterning of deliberate linguistic subversion by appealing to tension between the need to stand out and the need to remain intelligible. While we do not claim that language play is the only cause of linguistic change, our findings position language play as a constant source of new linguistic variants in very large numbers, a small proportion of which endure as changes. Our conclusion is that language play goes a long way toward accounting for linguistic innovations—with respect to where they come from and why languages change at all.
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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 | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".