Is One Innovation Enough? Leaders, Covariation, and Language Change
Bibliographic record
Abstract
Do the people who lead in one linguistic change, lead in others? Previous work has suggested that they do not, but the topic has not been addressed extensively with nonphonological, spoken data. This article answers this question through an examination of lexical, morphosyntactic, and discourse-pragmatic changes in progress in Canadian English as spoken in the largest urban center of the country, Toronto. Close scrutiny of the behavior of individuals across multiple linguistic variables (i.e., covariation) and using the Pearson product-moment correlation coefficient tests the use of incoming variants both by the community of speakers as a whole and by those who are leading change. The innovative variants of quotatives (be like), intensifiers (really, so), deontic modality (have to), stative possession (have), and general extenders (and stuff) demonstrate that the leaders of these multiple linguistic changes have common social characteristics (e.g., women lead more than one change), but it is not the case that any one individual in a community will be at the forefront of more than one change.
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.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".