Should We Analyse or Analyze British and American Spelling Doublets in Contemporary Canadian English?
Bibliographic record
Abstract
This paper analyses the use of British English and American English spelling doublets in contemporary Canadian English.The corpus analysis was conducted on the Corpus of Global Web-Based English (GloWbe) which contains approximately 1.9 billion words of text from twenty different English-speaking countries.This corpus was primarily chosen because it is the only balanced corpus available that allows direct comparison of BrE, AmE and CanE.The paper focuses on the three distinct categories of spelling doublets -those ending in (1) -our/-or, (2) -re/-er, and (3) -ise/-ize.Having selected ten most frequent words in each category, we proceeded to analyse their distributions in both spelling versions in all three varieties of English.The paper will show that Canadian speakers are under an enduring influence of their next-door neighbours despite the fact that Canadian normative grammars and dictionaries traditionally favour British orthographic norms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".