Becoming a major hub in the distribution of wine: Hong Kong as a gate to Asian markets
Bibliographic record
Abstract
This paper examines the conditions of emergence of a hub in the distribution of wine. We illustrate this through a detailed discussion of wine distribution in Eastern Asia and an examination of the case of Hong Kong as an emerging regional wine hub. Indeed, the Hong Kong Special Administrative Region (HKSAR) Government has imposed zero import tax on wine since June 2008. Since then, the city has attracted a large volume of wine from foreign countries and established a wine bond warehouse in the Asia-Pacific. In total, only 16% of the wine has served for local Hong Kong consumption, with 84% being transshipped to Macau and Mainland China. Well positioned at the heart of Chinese business diaspora with good global connections, Hong Kong is currently capturing important value from wine trade. But its position may be threatened by competing hubs (i.e. Singapore), if these are able to adapt to the needs of the rapidly changing market of wine. To analyze this situation, this paper uses the concept of agility, explaining how market knowledge, flexibility and responsiveness are key elements for regional competitiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".