DEBATES AND DEVELOPMENTS. Competitive Cities and Secure Nations: Conflict and Convergence in Urban Waterfront Agendas after 9/11
Bibliographic record
Abstract
Abstract In this exploratory article we investigate how longstanding ‘competitive city’ projects are actively reshaped by recent national security initiatives in urban waterfronts. We argue that port districts in large waterfront cities are becoming critical sites where actors are struggling to further different agendas. While proponents of competitive city projects appear directly concerned with promoting a particular vision of capitalist urban development in contrast to the national security agenda of port and border securitization, we contend that a simple dichotomy between ‘economy’ and ‘security’ cannot capture their complex intermingling. We examine the emergent public discourses of port (in)security in the US and Canada since 9/11, paying particular attention to the convergences between port security and waterfront gentrification initiatives, while also noting conflicts between these agendas. We identify four key areas of change: relations of power in the governance of port spaces, rationales of urban planning decisions, physical redesign of urban port spaces, and conflicts between ‘economy’ and ‘security’. Post 9/11 port security initiatives are sometimes at odds and other times at ease with the competitive city agendas that are readily apparent in urban waterfront redevelopments. Both projects have disturbing implications for social justice in waterfront cities.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.051 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| 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".