Peter Trudgill, The long journey of English: A geographical history of the language. Cambridge: Cambridge University Press, 2023. Pp ix, 177. Pb. £19.
Bibliographic record
Abstract
This is a book for anyone who wants to know about the origins and spread of the English language regardless of training. No need for technical knowledge of any kind – most linguistics books cannot be read in one sitting like a detective novel, whereas this one can. And the detective analogy is apt in terms of tracking when and whence English spread (quick spoiler: it has been spreading slowly over 1,600 years in numerous tiny fits and starts from the edges of land-masses to the regions within.) I read this book with Google Maps open, visiting the shores and islands settled by English speakers in date order (almost – St Kitts is an exception) as you can now virtually roam around parts of the Frisian Islands, the Angeln peninsula, the Channel Islands, Newfoundland, Labrador, Virginia, the Caribbean, the Bay Islands, San Andrés and Providencia, Chichijima, Hahajima, from Unst to Utila, and see the beaches on which they landed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.022 |
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".