The Present Perfect in Canada: is it more like football, rugby or ice hockey? A corpus-based comparative study with American English and British English
Bibliographic record
Abstract
The present study is set out to investigate on the use of the Present Perfect in Canadian English and to compare it with American and Britain English. The purpose of this MA dissertation is to find out if the Present Perfect in Canadian English is a mix of American or features or if it is Canadian. The present study is comprised of multiple corpora analyses, namely the Strathy Corpus, COCA and BNC, and a survey carried out among native speakers of these three varieties of English. Regarding the Present Perfect, different readings can apply to its usage: some of them include Continuance, Completion, Immediate Past, etc. In comparison with American and British English, it seems that the same verbs occur in the Present Perfect and that the same adverbs modify the Present Perfect. When modified by an adverb, it is accompanied by temporal adverbs. Frequency-wise, the Canadian and the American data appear to be the closest. However, different patterns are noticeable in Canadian English: it appears that activity verbs and communicative verbs are the frequent. Moreover, the survey analysis attests of a closer proximity between Canadian and American English. Therefore, the use of the Present Perfect in Canadian English is seemingly closer to American English than to British English but still exhibits numerous characteristics that indicate a Canadian flavor.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".