Smart city development in Zorrotzaurre, Bilbao. A case analysis
Bibliographic record
Abstract
The regeneration of Zorrotzaurre is the largest current urban development project in the city of Bilbao. Since the final Master Plan for the project was approved in 2012, progress in the development has already resulted in the opening of the Deusto canal and the construction and renovation of buildings on the northern and southern parts of the island. However, certain challenges have arisen throughout this phase that must be solved in order to guarantee further successful development of the island. These challenges can be summarised in four key factors: Governance, Talent Creation, Real Estate and Mobility. This report gathers a case analysis of six city districts in order to obtain pragmatic and robust findings and recommendations for the development process in Zorrotzaurre: Waterfront Toronto (Canada), 22@ -Barcelona (Spain), HafenCity – Hamburg (Germany), Innovation District- Porto (Portugal), Kalasatama -Helsinki (Finland) and Innovation District - Rotterdam (The Netherlands). The result of the analysis leads to seven core conclusions: Centrality multiactor spaces as governance structures for district development Holistic system for a bottom-up approach and citizens’ participation Comprehensive information sharing system Importance of district development facilitators and agency Living lab approach Compliance of real estate with social and environmental standards Mobility as an essential part of the district development process The analysis is part of Bilbao Next Lab, the action research project facilitated by Orkestra in collaboration with Bilbao City Council in order to advance within the smart specialization process of the city. According to the cogeneration model of action research, this report will be one of the contributions of the team of researchers from Orkestra to the process with the aim of defining specific policy instruments and actions for the development of the Zorrotzaurre district. Specific workshops, arranged by researchers, will take place in 2020 for such purpose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".