The Planning and Management of Responsible Urban Heritage Destinations in Asia
Bibliographic record
Abstract
Heritage in general, and in particular urban heritage in Asia, is at the focus of a perfect storm with populations growing rapidly in cities as the rate of urbanization continues to grow at an alarming rate. Together with populations increasing rapidly in cities as urbanization grows, there are ever larger numbers of tourists placing significant pressure on urban heritage resources. The Planning and Management of Responsible Urban Heritage Destinations in Asia explores issues such as: • Cities that are above their ‘carrying capacity’ and the damage caused to tangible and intangible heritage assets; • The need for a new management process to accommodate greater visitor numbers; • The move to stewardship as the approach for the future; • The ethical, social and regulatory issues surrounding the expropriation of heritage. The Planning and Management of Responsible Urban Heritage Destinations in Asia introduces the reader to the nature of the urban heritage, the pressures facing this heritage and the planning and management approaches and techniques that are available to deal with the realities facing the urban heritage. Essential reading for urban policymakers and planners, tourism officials, tourism industry professionals and heritage planners and managers as well as undergraduates and postgraduates of Tourism, Urban Planning and Sustainability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".