English language education and educational policy in Singapore
Bibliographic record
Abstract
Abstract This chapter provides an overview of English language education and policy in Singapore in relation to a world Englishes perspective, considering policy, practices, and ideologies. It takes a critical view of Kachru’s model as applied to Singapore English(es), noting the complexities of internal variation among Singapore’s English users, and how Singapore has moved from the Outer to the Inner Circle and thus demands a more nuanced framework. Analysis takes a discourse-analytic approach, anchored in Ruiz’s conceptualization of language orientations and applied to Singapore’s secondary English language syllabus. It considers how these orientations frame the narrative of policy, are operationalized into learning targets, and inform teacher practice. To understand further the position of English and Englishes in Singapore, the chapter draws on the Douglas Fir Group’s framework for second language acquisition (SLA), considering the mutually interactive forces of the macro (ideological), meso- (sociocultural/institutional) and micro-levels (human social interaction) involved in language-learning contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".