Reviving Forgotten Heritage as a Tourist Destination: A Case Study on Malcha Mahal Haunted Heritage Walks in Delhi
Bibliographic record
Abstract
Abstract Dark tourism, a phenomenon encompassing visits to sites associated with death, suffering, and historical atrocities, has gained increasing scholarly attention in recent years. This chapter explores the concept's historical roots, motivations for engagement, and its transformative impact on heritage sites, culminating in a case study of the Malcha Mahal Haunted Heritage Walks in Delhi. Despite being abandoned for centuries, this historic site gained prominence with the occupation by the self-proclaimed royal family of Oudh in the 1980s. The introduction of the Haunted Heritage Walk by Delhi Tourism aimed to harness the dark tourism potential of this previously neglected monument. However, initial challenges such as deteriorating conditions and safety concerns necessitated collaborative efforts with government departments for conservation and site preparation. Interviews with visitors revealed diverse motivations for attendance, ranging from thrill-seeking to appreciation of the monument's historical significance. Since the walk's inception, conservation efforts have commenced, accompanied by landscaping initiatives and amenities for visitors. While the future conservation and tranquility of Malcha Mahal remain uncertain amid increasing tourist interest, its transformation into an alternative destination within Delhi underscores the potential of dark tourism to revitalize neglected heritage sites. Sustainable tourism planning is essential to preserve the monument's unique features and manage tourism while maintaining its integrity for future generations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".