Bibliographic record
Abstract
The paper presents the results of the corpus-based research of noun cryptotypes in 20 varieties of English (Englishes). The data for this research collected from Mark Davies’ corpora GloWbE and NOW enabled us to focus on variation in the covert classification of nouns in modern Englishes. A noun cryptotype introduced by Whorf is approached as ‘a covert type of classification of nouns, marked by lexical selection in a syntactical classifier rather than a morphological tag’. The purpose of the study has been to compare and contrast the covert classification of basic 23 emotions in 20 Englishes (64,702 tokens). 20 Englishes have been clustered with the help of Data Mining methods (such as k-means clustering and a self-organizing Kohonen map). There are six clusters that appeared to be corresponding to geographic areas: American cluster (American and Canadian Englishes); Australian cluster (Australian and New Zealand Englishes); European cluster (British and Irish Englishes); Asian cluster (Indian, Pakistani, Singapore, Hong Kong, Malaysian, Bangladeshi, Sri Lankan, and Philippine Englishes); African cluster (Kenyan, South African, Nigerian, Ghanaian, and Tanzanian Englishes); Caribbean cluster (Jamaican English). The correlation coefficients among Englishes in the Asian and African clusters (the Outer Circle in the World Englishes Paradigm of Braj B. Kachru) range from 0.74 to 0.8 due to little contact among the varieties inside these clusters. The correlation coefficients between Englishes in the American, Australian and European clusters (the Inner Circle, Kachru) range from 0.92 to 0.933, which indicates a high consistency of these varieties owing to the long lasting, enduring linguistic contacts.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".