Official Languages and Bilingualism in the Courtroom: Hong Kong, Canada, the Republic of Ireland, and International Law
Bibliographic record
Abstract
Whilst Hong Kong is now part of China enjoying a high degree of autonomy, English continues to be the lingua franca of its education, commence, and legal process. The Chinese language (Cantonese) continues to be subjugated in Hong Kong's judicial and legal practice, even though it is constitutionally and by legislation recognised as an official language in the former British colony. In Re Cheng Kai Nam Gary, the defendant, facing trial which might, and ultimately did, lead to his imprisonment, sought that the judge presiding over his trial be able to directly understand Chinese with which he self-identified most intimately. Adopting a comparative approach by reference to and scrutinising Canadian, Irish, and international jurisprudence, this paper argues that an individual, on the basis of his or her language use rights and right to fair trial, is entitled to choose to use an official language with which he or she self-identifies most intimately in judicial proceedings affecting his or her rights and interests, and that fluency in the dominant official language and availability of interpretation during the relevant proceedings are immaterial and do not affect the individual's aforementioned rights. The author concludes that the unfortunate outcome and reasoning in Re Cheng Kai Nam Gary illustrated that the adverse effects of colonialism continue, in its altered neo-colonial ego, to be pervasive in post-colonial Hong Kong.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".