Bibliographic record
Abstract
ABSTRACT In this study we explored variation in the countability of nouns in Outer Circle, Expanding Circle and lingua franca Englishes, a phenomenon which is frequently cited as a marker of Inner Circle norms in TESOL and of endonormative and emerging varieties in the Outer and Expanding Circles. We inspected a set of mass nouns likeinformationandequipmentin the VOICE corpus and websites from Outer and Expanding Circle country domains. We also evaluated potential causes of variation, investigating differences between Outer and Expanding Circles and the contribution of substrate influence. Our data show notable and widespread countable use of nouns that are generally non‐count in Inner Circle Englishes, but such usage is highly infrequent overall. There appears to be greater variation in the Outer than the Expanding Circle, but little evidence of a determining role for substrate influence. We conclude that the prominence given to countability as a marker of ‘nativeness’ and ‘non‐nativeness’ is unhelpful, in both the prescriptive context of TESOL and the descriptive contexts of world Englishes and English as a Lingua Franca. We advocate the use of web‐based corpora to investigate lexico‐grammatical variation in lingua franca usage and to reveal the ‘plurilithic’ nature of English.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".