Resort Urbanism: Understanding the Power, Planning and Politics of Urban Development in Bávaro-Punta Cana, Dominican Republic
Bibliographic record
Abstract
In many developing countries, particularly Small Island Developing States (SIDS), coastal tourism impacts local economies and societies in significant ways, and plays an important role in transforming the environment. Rather than simply interpreting the changes that occur around coastal tourist enclaves as impacts, I argue that it is important to recognize the urban nature of spaces proliferating around these enclaves and to understand the role of the actors of coastal tourism in the production of these spaces. Utilizing a case study (Bávaro-Punta Cana, Dominican Republic), my research draws from over one year of fieldwork, including 97 semi-structured interviews with local residents, researchers, and various stakeholders. I examine the power relations between the private sector, local residents, and different levels and sectors of the State in the area’s urban development process. The context of Bávaro-Punta Cana is characterized by the following three issues. First, the newly established local government is generally impotent vis-á-vis other State actors and powerful private sector interests, and is unable to ensure the public good for its citizens. Second, many local residents live in conditions that are worse than traditional urban areas in the country. Third, the private sector has inserted itself as pivotal actor in the development and governance of the newly urbanizing area through practices of corporate social responsibility. Based on my analysis, I refer to the realities of Bávaro-Punta Cana as ‘resort urbanism.’ I utilize this term to illustrate the manifestations of tourism-related urbanization. Drawing from planning concepts, namely splintering urbanism, informality and citizenship, I analyze the physicality and spatiality of urbanization, the processes of urban development, and the rights of citizens and the relations between actors in the production of this new urban space. First, I argue that urban space in Bávaro-Punta Cana is produced as a form of ‘splintered urbanism.’ Second, this space is materially shaped by practices of informality or what is known locally as arrabalización. Last, the area lacks a local governance structure through which residents can adequately make claims to fulfill their basic needs, raising important questions regarding the rights and responsibilities of different actors in urban development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".