Bibliographic record
Abstract
It is never hard to find the latest news of the progress of this many-headed and foul-breathed monster English.No longer in its aquatic cave awaiting the arrival of Heracles, it now strides across the earth, devouring school systems, reorganizing social relations and polluting minds.The newest country on the planet, the Republic of South Sudan, for example, has announced that the sole language of secondary education will be the official language -English.And from there, as is all too often the case, the gradual downward creep of English may start, so that elementary schools will have to introduce English, in order to prepare students for secondary education.The increasing pressure to provide better access to English language resources broadens the base of English education.In Korea, which has perhaps been gripped by 'English frenzy' (yeongeo yeolpung) more than any other country (see Park, this volume), this downward pressure now means that the average age of children starting to study English in and around Seoul is 3.7 years (Bai, 2011).This average figure accounts both for the 7.3% of children between the ages of three and five that have not yet started studying English, the 6.6% who begin learning English even before they reach two years of age, as well as the 1.3% of mothers who have been opting for so-called antenatal English education.Along with such early learning, Korea is now home to 'English villages', replete with castles, post offices and native speakers (or, at least, blond Caucasians, who look, in this racialized concept, like native speakers should).And if Koreans aren't importing English, they are exporting themselves: more than 40,000 school-aged Korean children -known as jogiyuhaksaeng (early overseas students) -are studying in the US, Canada, the UK, Australia, New Zealand, Singapore, the Philippines, Malaysia and elsewhere.Typically, the young Korean children go to live overseas with their mothers and 'this particular form of separated family is referred to as a "wild geese" family, who live apart so that they can educate their children in English-speaking countries' (Jeon, 2010: 59).For some students, this works; for others, they struggle to achieve adequate academic English in school, fall behind in their knowledge of Korean and end up somewhere inbetween.Meanwhile, in Singapore, the former Prime Minister Lee Kuan Yew has announced that he feels the American version of English will probably
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.116 | 0.077 |
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".