THE NORTH AMERICAN REGIONAL VOCABULARY SURVEY: NEW VARIABLES AND METHODS IN THE STUDY OF NORTH AMERICAN ENGLISH
Bibliographic record
Abstract
This paper presents the results of a new survey of lexical variation in North American English, called the North American Regional Vocabulary Survey(NARVS). Apart from introducing many new variables that have not been previously studied, the paper examines the use of two quantitative methods,net variation and major isoglosses, as ways of distinguishing the most important regional lexical divisions and the most powerful lexical variables from regional divisions and variables of lesser importance. The quantitative analysis motivates several conclusions. English-speaking Canada is shown to comprise six principal lexical regions:the West, Ontario, Montreal, New Brunswick-Nova Scotia, Prince Edward Island,and Newfoundland. A list of the most powerful variables for distinguishing Canadian regions is presented, headed by the set of regional terms for a`house in the country where people go on summer weekends' (cabin,cottage, etc.). A similar analysis of lexical differences across the Canada-United States border is developed, which concludes that no region of Canada can be reliably distinguished as relatively more American than any other and that Canadian regions have more in common at the lexical level with each other than any of them has with the United States.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".