A Corpus-Based Study on Construction of “Anger Adjectives + Prepositions” in World Englishes
Bibliographic record
Abstract
Anger as one of the basic emotions has attracted much attention. In the construction of “Anger adjectives + prepositions”, the temporal duration of the Anger adjectives is closely related to their prepositional collocates. Differences in the use of the Anger adjectives and their prepositional collocates might be captured in the world English varieties. The corpora used in this study cover eight varieties of English. The five varieties of English used in Canada, Philippines, Singapore, India and Nigeria are from the International Corpus of English (ICE). The China English corpus (ChiE) consists of news texts crawled from six Chinese English media. American English is taken from the Corpus of Contemporary American English (COCA) and British English is taken from British National Corpus (BNC). By investigating the use of the Anger adjectives and their prepositional collocates in the eight varieties of English, this paper finds that, on the continuums of the temporal duration of Anger adjectives, most varieties of English are closer to American English, whereas only Singapore English is close to British English. The distribution of Anger adjectives in the English varieties is largely in accordance with the Concentric Circles of world Englishes whereas the continuums of the temporal duration of emotions present a new insight into their relations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".