Building a ‘cultural city’: Heritage, identity, and the politics of reconstruction in Bhaktapur, Nepal
Bibliographic record
Abstract
Nepal's devastating 2015 earthquakes prompted anxious attention to United Nations Educational, Scientific and Cultural Organisation (UNESCO)-designated World Heritage sites in the Kathmandu Valley and the means and methods for rebuilding damaged monuments and residences. As a value-loaded concept, the debate over ‘heritage’ today lays bare tensions between the national government and local authorities over the use of foreign experts, building techniques and materials, and the appropriate aesthetics for heritage reconstruction. Drawing upon ethnographic data, archival material, and photo documentation from the city of Bhaktapur – one of the three major urban areas of the Kathmandu valley – we trace how local autonomous political power and heritage management unfolded interdependently over time. By considering contestations over the styles of private houses, public temples and former royal palaces, we show how heritage becomes the site of power struggles between a municipality and the national government, between the locally powerful Newar Indigenous community and the nationally dominant caste Hindu hill elite, between private homeowners and public officials. Our findings build on the values-based approach to heritage studies through a focus on heritage as the material manifestation of community values, but also the site where those values are continually contested.
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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 | 0.002 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".