Not always variable: Probing the vernacular grammar
Bibliographic record
Abstract
Abstract Written and spoken language are known to differ substantially (Biber, 1988; 1995; Biber, Johansson, Leech, Conrad, & Finegan, 1999). Standard written language is highly uniform and governed by prescription, whereas the vernacular is most revealing of structured heterogeneity (Weinreich, Labov, & Herzog, 1968). We focus on four English morphosyntactic variables that problematize assumptions about the nature of variation in the vernacular: the genitive, the comparative, the dative, and relative pronouns. Each is characterized in casual speech by functional divides that reflect discrete configurations of variant use. After detailing the patterning of these variables in speech, we explore a characteristic arguably shared by each: its historical pathway into the language, where analogy and prestige were powerful motivations for variant choice. We suggest that this combination of systemic and social factors contributed to the nature of these variables in the vernacular grammar. Furthermore, we advocate for greater scrutiny of written and spoken data and the outcomes of change from above and below within each register. The type of innovation and its trajectory may affect the nature of the emergent variable grammar.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".