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Record W4386847398 · doi:10.1353/lan.2023.a907010

Language Play is Language Variation: Quantitative Evidence and What it Implies About Language Change

2023· article· en· W4386847398 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLanguage · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVariation (astronomy)SubversionLinguisticsLanguage changeNounPosition (finance)Constant (computer programming)PsychologyComputer sciencePhilosophyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.080
GPT teacher head0.406
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it