One Country, Two Systems, Three Languages: A Proposal for Teaching Cantonese,<i>Putonghua</i>and English in Hong Kong’s Schools
Bibliographic record
Abstract
Combining local, national and international languages — the last almost exclusively English in fact —is one of the most pressing issues facing educational professionals in today’s world. How and when can English be introduced to the school curriculum so that children still master literacy in local and national languages? Hong Kong’s language policy aims for its citizens to be trilingual in Cantonese, Putonghua and English and biliterate in Chinese and English. The current policy is that Cantonese should be the medium of instruction for all government primary schools, where English and Putonghua are taught as subjects. The majority of secondary schools are also Chinese medium, but about a quarter are English medium schools. Recently the government has signaled a possible controversial change in policy, as it has authorized a trial for ‘Chinese subjects’ to be taught through Putonghua in selected schools. At the same time ‘consumer’ demand for English is constantly heard. This paper will propose a ‘multilingual’ solution to the medium of instruction policy. First, medium of instruction policies and practices in Hong Kong will be reviewed and then possible future developments will be considered. Finally, a way of combining the local language (Cantonese), the national language (Putonghua) and the international language (English) in the school curriculum will be proposed.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".