Sustainable Urbanism in Abu Dhabi: Transferring the Vancouver Model
Bibliographic record
Abstract
:Notwithstanding the role of cross-national transfer of planning knowledge globally, there is a dearth of in-depth research that examines the transfer process. In recent years, Vancouver has garnered international acclaim as one of the world’s emerging “eco-cities,” and the planning model applied there has begun to be emulated in numerous foreign contexts. As the capital of the United Arab Emirates and an emerging global business center, Abu Dhabi has sought the expertise of leading Vancouverite planners to develop a design and regulatory framework that is culturally distinctive. This article examines the knowledge transfer process with particular emphasis on the authority hierarchies formed and their impact on the adaptation of transferred knowledge. Using in-depth interviews and an analysis of the planning documents, this article provides a micro-level investigation of the interpersonal and institutional relationships. Such relationships explicate the tools utilized by the transfer agents in their attempts to contextualize, adapt, and operationalize imported knowledge. We propose a model that maps the various levels of authority and links them to the knowledge flow between local acquiring agents and foreign transferring agents. The application of this model to the Abu Dhabi scenario reveals a complex web of interactions between the local and foreign planners involved, across multiple institutional levels.
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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 | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".