This monograph analyzes the scope of the core vocabulary of three major varieties of modern written English: American English in the Brown Corpus, British
Bibliographic record
Abstract
Corpus. The purpose of this University of Manitoba dissertation is to determine the lexical items that are statistically stable across all three varieties. The author argues that there is a basic set of lexical items which occur in all three varieties, and that determining this set will be of importance not only to linguists but also to authors of teaching materials, and to language instructors. The book comprises 304 pages, over two hundred of which are appendices of various types – there are over forty appendices in the book in the form of lists of statistically significant word groups as well as lists of word groups that did not reach statistical significance. There are nine chapters: five of them serve as introductory reading for the study proper, which begins in Chapter 6. The study introduces concepts such as corpus design and corpus representativeness in Chapter 2. Unfortunately, one gets the feeling that some time passed between the writing of the manuscipt and its publication, as numerous references to corpus design and corpus linguistics in general are missing. In Chapter 4, the various corpora on the original ICAME CD are introduced, but mention is
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".