THE NORTH AMERICAN REGIONAL VOCABULARY SURVEY: NEW VARIABLES AND METHODS IN THE STUDY OF NORTH AMERICAN ENGLISH
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
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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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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