Implications of Cultural Heritage in Urban Regeneration: The CBD of Dar es Salaam
Bibliographic record
Abstract
Cultural heritage is an inherent element of the urban landscape that evolves along with the cities. It is widely recognized that a solid coordination in the management of cultural heritage and urban transformation contributes to more effective planning interventions. However, a big gap between both fields still persists. In the Central Business District (CBD) of Dar es Salaam, Tanzania, the lack of coordination is evident. In recent years, historical buildings have been demolished and high-rise buildings are being constructed, whereas initiatives for protecting the built heritage have emerged and successfully restored emblematic buildings. The present paper, based on a morphological analysis and in-depth interviews, discusses how the interventions for the transformation of the CBD of Dar es Salaam are being implemented and the role that cultural heritage plays in the process. This paper also suggests ways in which cultural heritage can contribute to the urban regeneration of the area, highlighting the relevance of intangible cultural heritage as a fundamental aspect that determines the life and identity of the communities and that has to be carefully considered in order to achieve inclusive and holistic urban regeneration processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".