The paradox of a tourist centre: Hong Kong as a site of play and a place of fear
Bibliographic record
Abstract
Economic globalization and advances in transport and communication technologies are central to increased travel. Travel by business people, tourists, and diasporas has stimulated the mobility of capital, goods, services, people, knowledge, and disease from one location to another (Urry 1995: 173; chapter 18 in this volume). Until the terrorist attacks of 11 September 2001, international travel for both play and work had taken off in an upward direction. This event adversely affected passenger numbers and miles travelled on a global scale. The SARS outbreak in spring 2003 did not have the same global impact but did severely affect travel to and from Hong Kong, some other Asian cities, and Toronto. Largely in seeking to capture these mobile flows, cities, regions, and nation-states compete to imagine and sell their historical image and locational advantages to international investors and tourists in an ever-tightening global economy (Kearns and Philo 1993; Short and Kim 1999; Hall 2000). Within the tourist industry, diverse destinations are constructed to capture the flow of tourists and to direct them to specific sites of play (Judd and Fainstein 1999). These destinations often involve the construction of various forms of ‘adventure’ and/or ‘otherness’ that can ‘spice-up’ the experience of specific tourist play (Hooks 1992). This chapter specifically examines Hong Kong before and after the 1997 transition from British colony to Special Administrative Region (SAR) of the People’s Republic of China, and the changing nature of its East-West play within the global tourist 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".