A Synopsis of the Lexical Variations in British and American English
Bibliographic record
Abstract
The focal point of this research work is to find out the lexical distinctions between British and American varieties of English and their persistently reciprocal impacts. Over the years, both the British and American vocabularies have been influencing each other, specially due to politics, economics, diplomatic relationships, information technology and globalization. From time to time, the variance of vocabulary in the two varieties results in increasing the number of English synonyms which is, in fact, an asset for the language. But sometimes, the synonyms may cause serious differences in meaning as some words may mean something in British English; the same may denote something else in American English and vice versa. One should, therefore, be careful and consistent about their use.It is most probable that as a result of various procedures of the change in lexical meaning and language, the two varieties would someday merge by turning them into an identical entity. So, this paper intends to provide the reader and/or the user of English with the correct application of the two varieties for our day-to-day life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".