Plurality and world Englishes: The social realities of language use
Bibliographic record
Abstract
ABSTRACT In spite of decades of scholarship on plurality and in particular Englishes, discussion in some academic quarters in recent years has questioned this fundamental aspect of language/English use, with some coming to view plurality (of Englishes) in fact as marginalizing and segregating (cf. Bruthiaux 2003; Pennycook 2003, 2008; Saraceni 2010; Ur 2010). In light of now nearly four decades of scholarship on world Englishes (WE), by Yamuna Kachru as well as innumerable other scholars, this paper revisits the core concepts that form the foundation for plurality and consequently WE, concepts that must be addressed if plurality (of Englishes) is to be challenged. The discussion revisits the numerous foundational concepts in (socio)linguistics for WE, and gives particular consideration to the notion of the speech community in light of the higher degree of mobility and virtual interaction in the contemporary era.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".