How register and region shape the language network: evidence from Computational Construction Grammar
Bibliographic record
Abstract
While Construction Grammar has proven effective at modelling regional and register variation separately, it has seldom been used to explore the interaction between the two. The present paper fills this gap by applying a Computational Construction Grammar framework to a collection of large English corpora, including two digital registers (written tweets and spoken YouTube transcripts) and five inner-circle varieties (US, UK, Canada, Australia, and New Zealand). We show that constructionist principles successfully capture a range of register- and region-based distinctions across the grammar, and we report the novel finding that both sources lead to systematic, largely independent patterns of variation. Specifically, register effects are more pervasive and concentrated in abstract, high-level constructions, while regional effects are relatively sparser and manifest most prominently in lower-level, surface constructions. To account for these results, we hypothesise that register and regional associations operate along a continuum of constructional ‘salience’: while the former require the explicit learning of variants for communicative functions, the latter begin as products of exposure before they can acquire indexicality. We conclude with implications of our study for a more comprehensive model of variation in the language network, as well as for future endeavours towards intersecting Construction Grammar and sociolinguistic theory.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".