Casino Capitalism and Its Legitimacy Impact on the Politico-administrative State in Macau
Bibliographic record
Abstract
Casino capitalism has its dialectical tendencies in Macau. On the one hand, it stimulates economic growth, provides employment, and strengthens the post-colonial state in Macau during the period of economic boom. On the other hand, casino capitalism can widen the income gap between the rich and the poor, generate addictive gambling, and de-legitimize the post-colonial state in Macau during the global and regional economic downturn. The weaknesses of the politico-administrative state in Macau, including the absence of institutional checks and balances, the frail civil society and the relatively docile mass media, have magnified the negative impacts of casino capitalism on Macau. In response to the negative ramifications, the Macau government has taken measures to be more interventionist, to enhance social welfare, and to prepare contingency plans that would tackle the sudden bankruptcy of any casinos. The central government in Beijing also displays contradictory considerations when it deals with Macau's casino development, supporting the casino industry while simultaneously encouraging the Macau government to diversify its economy. Overall, casino capitalism not only has contradictory impacts on the Macau city-state but also reveals the inherent contradictions of Beijing's policy toward the territory's over-dependence on the casino economy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".