Mapping out particle placement in Englishes around the world: A study in comparative sociolinguistic analysis
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.
Bibliographic record
Abstract
Abstract This study explores variability in particle placement across nine varieties of English around the globe, utilizing data from the International Corpus of English and the Global Corpus of Web-based English. We introduce a quantitative approach for comparative sociolinguistics that integrates linguistic distance metrics and predictive modeling, and use these methods to examine the development of regional patterns in grammatical constraints on particle placement in World Englishes. We find a high degree of uniformity among the conditioning factors influencing particle placement in native varieties (e.g., British, Canadian, and New Zealand English), while English as a second language varieties (e.g., Indian and Singaporean English) exhibit a high degree of dissimilarity with the native varieties and with each other. We attribute the greater heterogeneity among second language varieties to the interaction between general L2 acquisition processes and the varying sociolinguistic contexts of the individual regions. We argue that the similarities in constraint effects represent compelling evidence for the existence of a shared variable grammar and variation among grammatical systems is more appropriately analyzed and interpreted as a continuum rather than multiple distinct grammars.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it