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Record W2120358958 · doi:10.1068/a38263

Collaborative Partnerships for Urban Development: A Study of the Vancouver Agreement

2007· article· en· W2120358958 on OpenAlexaboutno aff
Michael Mason

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsnot available
Fundersnot available
KeywordsDowntownGeneral partnershipPublic administrationContext (archaeology)Corporate governancePolitical scienceAccountabilityUrban planningPoliticsEconomic growthRegional sciencePublic relationsSociologyManagementGeographyEngineeringEconomics

Abstract

fetched live from OpenAlex

Collaborative partnerships—featuring intergovernmental and/or public–private sector cooperation—have been identified as a leading organisational expression of the ‘new urban governance’. The paper examines the Vancouver Agreement—an urban development compact between the governments of Canada, British Columbia, and the City of Vancouver. Signed in March 2000 for a five-year term, and renewed in April 2005, the Vancouver Agreement has been widely acclaimed as an example of successful collaborative working, addressed to the revitalisation of the city's Downtown Eastside. The origins of the agreement are explained in the context of an urban crisis in the Downtown Eastside, where established policies were seen to be failing. High-level political support for a new governance approach led to the adoption of an urban development partnership, and the paper sets out its structure and strategic programmes of action. Benchmarked against conditions for effective intergovernmental working posited in the public administration literature, the paper then analyses five procedural attributes of the partnership: resource sharing, leadership, community involvement, mutual learning, and horizontal accountability. Concluding observations are offered on the long-term prospects for the Vancouver Agreement.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.365

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 studies0.0000.000
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 head0.058
GPT teacher head0.326
Teacher spread0.268 · 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 designNot applicable
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

Citations29
Published2007
Admission routes1
Has abstractyes

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