Bibliographic record
Abstract
This is a post-mortem on Malaysian TeSME (Teaching of Science and Mathematics in English) program based on its comparison with Canadian immersion programs. Malaysia and Canada have some common sociological aspects such as the size of population, the ratio of indigenous people and immigrants, and multilingual contexts. It also has in common various core elements in the set of criteria proposed by Swain and Johnson (1997) to define a prototypical immersion program. Thus, the lessons Canadians have learned from immersion may be seen as significant guiding light for TeSME and other attempts of content-based instruction programs. Canadian immersion has been different from TeSME at least in terms of three core features: overt support exists for the L1; the teachers are bilingual; and the classroom culture is that of the local L1 community. These differences made four issues more prominent: Learning outcome of TeSME; mainstay of TeSME; judicious use of L1; and function of TeSME. Finally some suggestions are proposed: give higher priority to promoting concept development across languages for now; make English classes more effective; promote bilingualism in TeSME; and extend TeSME’s function to understanding and integrating other cultures and languages.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.099 | 0.023 |
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".