Bibliographic record
Abstract
This paper starts with the question of why the outcome of English instruction in Korea remains unsatisfactory despite the prominent introduction of changes in English Education in the 90's. This question leads to the comparison of Baker's FLT and IP as a weak form and a strong form of education for bilingualism, respectively. On the basis of what is observed in this comparison, this paper points out that FLT is greatly responsible for the failure in most cases of English Education in other countries as well as in Korea's English Education. As an alternative to FLT, this paper aims at supporting the expansion of IP, which was originally introduced in Canada more than 40 years ago, spread to many other countries, and is being seriously taken into consideration in Korea. It will be further pointed out that some potential concerns for students' low outcome in their first language skills and content areas that may happen in the implementation of IP would be solved or at least reduced if core features of IP are well abided by as they stand.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".