Bibliographic record
Abstract
What are we to do with english? Of all the major languages of the world, it causes the most anxiety. Its words seem to want to invade the citadels of other languages, forcing institutions such as the Académie Française to call for barricades against it; in the enclaves of Englishness, a Celtic fringe struggles to hold on to the remnants of the mother tongue; and in most parts of the world those without the ostensibly anointed language often see themselves as permanently locked out of the spring-wells of modernity. Sometimes the global linguistic map appears to be a simple division between those with English and those without it. In the reaches of the former British Empire, a swath of the globe stretching from Vancouver east to the Malay Peninsula, English has come to be seen as an advantage in the competitive world of global politics and trade; in the emerging powers of East Asia, most notably China and South Korea, the consumption of global English is evident in the huge sale of books on English as a second language; in parts of the world traditionally cut off from English, including eastern Europe, the mastery of the language marks the moment of arrival. Most linguistic research on English is carried out in institutions in the Germanic and Nordic zones of northern Europe. In popular books on language and in serious linguistic studies, a powerful myth of English as the global language has taken hold. We are presented not with a world at the end of history but with one in which English sits at the center of a new global community: “English-speaking people and their culture are more widespread in numbers and influence than any civilization the world has ever seen,” claims Robert McCrum (257).
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.061 | 0.023 |
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".