Bibliographic record
Abstract
English and Other Languages Jespersen (1948) ascribes the tremendous variety of the English language to the freedom a writer was given in England to "... take his words where he chooses, whether from the ordinary stock of everyday words, from native dialects, from old authors, or from other languages, dead or living. The consequence has been that English dictionaries comprise a larger number of words than those of any other nation, and that they present a variegated picture of terms from the four quarters of the globe." (Jespersen, 1948: 15). The foreign words and phrases so abundantly present in English have immeasurably enriched the language. English not only easily incorporates foreign words but also assimilates syntactical elements from other languages. This feature of English is the very reason for its rapid evolvement into a world language. The English literatures of Canada, Australia, South Africa, the United States of America, New Zealand and other English-speaking countries have all succeeded in describing situations, backgrounds and personalities typical for their territories and often better than any other language could have done. In fact, English has become so integrated in certain countries that one describes the particular local brand of English as "South African English usage", "Australian English", "American English", etc. In South Africa, for example, it would be quite normal to find words from Afrikaans incorporated in an English text, the reader turning a blind eye to these: "If you'll wear your nagmaal jacket next time ... I'll be glad to show you all over my farm where I'm not going to plant potatoes ... That is, among the haak-en-steek thorns." (Bosman, 1971).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.029 |
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".