Language Policy, Language Teachers' Beliefs, and Classroom Practices
Bibliographic record
Abstract
The widespread use of a local variety of English, Singapore Colloquial English, or Singlish, has become somewhat of a controversial issue in Singapore especially in the eyes of the Singapore government. For example, in 2002 the Singapore government launched The ‘Speak Good English Movement’ (SGEM) with the objective of promoting the use of Standard English among Singaporeans. Furthermore, Singapore's newspapers have recently suggested that the responsibility for halting the deterioration (perceived or real) of the standards of English rests with Singapore's English language teachers. The case study presented in this paper offers one lens from which to view a policy-to-practice connection by outlining the impact of language policy on the beliefs and classroom practices of three primary school teachers concerning the use of Singlish in their classrooms. The results confirm those of previous studies that teachers’ reactions to language policy is not a straightforward process and as such it is important to understand the role teachers play in the enactment of language policy.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".