Endonormative stabilization in Philippine English lexis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In the past 10 years, we have seen an enormous increase in the interest in Philippine English (PhilE) vocabulary. This is especially documented by the new entries of distinctive PhilE words in the Oxford English dictionary . Thanks to the rise of electronic mega‐corpora, such as GloWbE and the NOW Corpus, it has become possible to discover even more lexical innovations in PhilE. In this article, I compare nominal and adjectival suffixes across the following varieties of English: India, Singapore, Hong Kong, the Philippines, the United States, Canada, New Zealand and Great Britain. The comparisons were carried out on a quantitative and qualitative level, based on the findings in GloWbE, NOW and ICE. Apart from suffixes, the article also discusses less frequent and productive types of word‐formation, such as synthetic compounds and splinters ( budgetarian ).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it