Hong Kong’s new normal: Remaking authorized discourses of “special administration,” 2017–2022
Bibliographic record
Abstract
The paper presents a critical discourse analysis of the five annual policy addresses of Hong Kong's chief executive, Carrie Lam Cheng Yuet-Ngor, whose term of office (2017-2022) spanned the most tumultuous episode in the modern history of the territory, one bisected by an extended season of street protests and the subsequent imposition of a national security law. Read as an authorized, real-time transcript of governmental rationalizations and responses, delivered in the first-person voice of Hong Kong's most senior politician, the annual policy addresses narrate a crisis-mediated shift from a postcolonial redoubt of liberal capitalism to an outpost of China's national-security state. Although the "one country, two systems" formula enshrined in Hong Kong's Basic Law retains its official status, a distinct transformation is evident, from the evocation of "two systems" as a wedge against one-country assimilation, to an apparently new normal based on the principles of securitized, "one-country" integration. In the process, the chief executive increasingly embraced the received discourses and sanctioned priorities of the Chinese Communist Party in her depictions of a "New Paradigm" for the governance of Hong Kong, founded on the "steer of the Central Government" with the "cooperation" of the government of the Special Administrative Region.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".