Bibliographic record
Abstract
English for Academic Purposes (EAP) is a truly international phenomenon, linked in with the overall trend towards the globalisation of information exchange, communication and education. English is now well established as the world language of research and publication and an ever-greater number of universities and institutes of learning are using English as the language of instruction. With this tremendous expansion, there has been a parallel growth in the preparation of non-native English speakers (NNSs) for study in English. This has taken place, and is continuing, in English-speaking countries such as the United States, Canada, Great Britain and Australia, post-colonial territories such as South Africa, Zambia, Malawi, Hong Kong, Singapore and the Arabian Gulf States, and other countries where English has no official status, such as China, Japan, continental Europe and South America. In recognition of this important development, it is time that serious attention was given to EAP in a scholarly work. The purpose of this book is to highlight the various key issues of the field and to demonstrate, through specially commissioned articles from leading scholars in the field, the scope, theoretical issues and pedagogical concerns of EAP. Individual contributions are original research articles, taking a broad deinition of research to include philosophical enquiry and critical review. The majority of the chapters, however, contain empirical studies. Contributors have been chosen by reason of their track record in the EAP ield, based on a literature review of the leading books and journals that deal with EAP.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.496 | 0.317 |
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".