Bibliographic record
Abstract
English, the lingua franca of the Commonwealth, is also the World's most influential language in human history. The influence of English as the language of academe may be reflected in the fact that more books about all the religions of the world have probably been published in English than in any other language on earth. This is perhaps because the former British Empire has always had to deal with a multiplicity of religions within its dominions. Today, the Commonwealth of Nations comprises more than a third of the Muslim population of the world—lodged in Pakistan, Bangladesh, India, Nigeria, and elsewhere.2 2. Statistics on religious demography may be found in the US State Department's 2006 Report on International Religious Freedom. Available at http://www.state.gov/g/drl/rls/irf/2006/index.htm (accessed 31 March 2007). The Commonwealth also includes about a third of the Christian countries of the world—in Africa, the Caribbean, Britain, Canada, Australia, and New Zealand, and the Anglophone world of the Pacific. This article addresses such paradoxes as to why Muslims are well represented in the Commonwealth while Arabs are not, and why Christianity is under siege globally while the English language is the dominant international language.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".