Bibliographic record
Abstract
ABSTRACT Updates on the statistics of English. Starting with excerpt from David Crystal, How many millions use English? ( ET 1, 1985). The author says: Reading this article again, that almost a quarter of a century on, the most noticeable change, it seems to me, has been in the amount and colour of the author's hair! That aside, I am struck by my final comment: ‘I shall stay with this figure for a while’ – a billion. It appears I stayed with it for a decade. In the first edition of my English as a Global Language (1997: 61) I raised my estimate, suggesting a middle-of-the-road figure of 1,350 million. In the second edition (2003: 69), a ‘cautious temperament’, I said, would suggest 1,500 million. And these days, having read the more sophisticated assessments by David Graddol and others, I am prepared to revise upwards again in the direction of 2 billion. In short, we have moved in 25 years from a fifth to a quarter to a third of the world's population being speakers of English.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.175 |
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".