The Lisbon Waterfront: Perspectives on Resilience in the Transition from the Twentieth to the Twenty-First Century
Bibliographic record
Abstract
For forty years, the port city of Lisbon, Portugal, has been trying to be resilient by adapting to technical changes in port activities. Today it continues to seek to formulate long-term strategies to improve the port and create a stronger maritime community. Looking at regional planning strategies, this article analyzes the city’s ability to face adversities brought by the decline of the port. Between 1973 and 2013, central and local government authorities proposed a number of plans. Each plan set out to relocate port facilities, and to redevelop and improve waterfront territories for the community and the environment, in order to become competitive enough to attract global stakeholders and boost economic activities. The fate of each plan depended on the central government, regional bodies, the municipalities of Lisbon and surrounding towns, and the Lisbon Port Authority, all part of the Lisbon Metropolitan Area around the Tejo estuary. But the port city has repeatedly failed to carry out most of these plans, and it has not attracted new investment. It has failed to formulate and establish a coherent planning strategy. For over four decades, no one has been able to develop necessary solutions to expand the port, relocate container terminals, and remain competitive. The discussion of each of the five plans presented here will explore why Lisbon has struggled and how those struggles have threatened Lisbon’s resilience as a port city, that is to say, its ability to recover readily from adverse conditions, even if in a new form.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.038 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".