Canadian Dollar in the English Language Varieties: Corpus‑Based Study
Bibliographic record
Abstract
The slang name for Canadian dollar loonie is a Canadianism used not only in spoken (Boberg, 2010: 121), but also in written texts such as Canadian news articles. While loonie is obviously taken for granted by Canadians, its occurrence in English texts published beyond Canada has hardly been in the focus of corpus-based studies. The goal of this study is to find out in what Canadian English written texts loonie occurs and whether it is encountered in the other varieties of English by researching the corpora adapted for web access at Brigham Young University (BYU), the Strathy Corpus of Canadian English (SCCE), the Corpus of Contemporary American English (COCA) and the corpus of Global Web-Based English (GloWbE). The first two corpora were searched to reveal the genres of the written texts loonie occurs and GloWbE – to see loonie used in the other varieties of English. The obtained results revealed that loonie occurs in such written texts as newspaper and magazine articles of SCCE and COCA predominantly in the contexts connected with money issues. Search of GloWbE showed the use of loonie in American and British mass media texts, which reveals that this Canadian slang name goes beyond Canadian texts and thus, as Davies (2005: 45) has stated ‘[...] few of us are cocooned from [...] vocabulary of the major international varieties of English’. These findings therefore call for more detailed research of the collocations containing loonie in various text types of different varieties of English.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".