Challenges in Conserving and Managing Heritage in Asian Urban Areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter builds on the ethical setting presented in Chapter 1. It discusses the challenges facing residents in Asian urban areas and provides an overview of the context in which urban planning, design and management in Asia occurs. Based on this analysis, the chapter then examines the problematic nature of the interface between heritage conservation and the planning, design and management of urban areas, including the impact of tourism in heritage environments. This discussion will provide the context for the case studies and examples that follow in Chapters 3, 4 and 5. Urban areas in Asia vary in size and include metropolitan areas, regional towns and large cities, as well as portions of cities, such as historic districts. They also differ in terms of geography, economic conditions, political systems and cultural and social systems. Given these differences, it is difficult to generalise about them. However, Asian urban areas have certain similarities, one of which is that their populations are growing at unparalleled rates (see Figure 2.2). Also, the populations of these fast-growing and rapidly-expanding urban areas are demanding infrastructure development at a pace that is often far beyond local economic, financial and human capacities. Such urban areas often have poorly-developed urban management structures and weak decision-making systems, however. Furthermore, these cities have planning and governance approaches that are neither comprehensive nor robust enough to deal with the growth and complexity of the urban situation. In the very largest cities, the limits of liveability have been reached and they are unsustainable in their current form.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it