Web-Corpora from Top-Level Domains Represent National Varieties of English
Bibliographic record
Abstract
In this study we consider the problem of determining whether an English corpus constructed from a given national top-level domain (e.g.,.uk,.ca) represents the national dialect of English of the corresponding country (e.g., British English, Canadian English). We build English corpora from two top-level domains (.uk and.ca, corresponding to the United Kingdom and Canada, respectively) that contain approximately 100M words each. We consider a previously-proposed measure of corpus similarity, and propose a new measure of corpus similarity that draws on the relative frequency of spelling variants (e.g., color and colour). Using these corpus similarity metrics we show that the Web corpus from a given top-level domain is indeed more similar to a corpus known to contain texts from authors of the corresponding country than to a corpus known to contain documents by authors from another country. These results suggest that English Web corpora from national top-level domains may indeed represent national dialects, which in turn suggests that techniques for building corpora from the Web could be used to build large dialectal language resources at little cost.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".