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DEBATES AND DEVELOPMENTS. Competitive Cities and Secure Nations: Conflict and Convergence in Urban Waterfront Agendas after 9/11

2006· article· en· W1832331872 on OpenAlexaffabout
Deborah Cowen, Susannah Bunce

Bibliographic record

VenueInternational Journal of Urban and Regional Research · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsYork University
Fundersnot available
KeywordsPort (circuit theory)Urban planningCorporate governancePolitical scienceSociologyPolitical economyEconomyEconomicsManagement

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.350
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2006
Admission routes2
Has abstractyes

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