Bibliographic record
Abstract
The terms “center” and “periphery” have a special significance for the English language given how widely it is learned and used around the world today. Various powerful centers of English use have tremendous attraction and influence. In turn, these centers render other English‐using communities to a relative peripheral status, thus creating a complex set of dynamics and tensions between communities. The concept of World Englishes captures this complexity in identifying the Inner Circle, where English historically has functioned as an L1, and the multilingual, demographically superior Outer and Expanding Circles, where English has become an additional language as a consequence of British colonial rule and greater transnational interaction. For native and non‐native English‐speaking teachers (NESTs and NNESTs) around the world, these center‐versus‐periphery dynamics have important ramifications for curriculum development and pedagogical practice. They further have personal relevance for NNESTs, who are the vast majority of instructors and are themselves from the Outer and Expanding Circles.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".