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Record W2286605934 · doi:10.14288/1.0088035

Sustainable development in the Vancouver-Seattle corridor: a system for transborder planning

2009· article· en· W2286605934 on OpenAlexaboutno aff
Steve Jay Patrinick

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental planningSustainable developmentUrban planningGeographyPolitical scienceEngineeringCivil engineering

Abstract

fetched live from OpenAlex

The implementation of planning goals and objectives, including the goal of regional sustainability, requires cooperation and a degree of social unity, or cohesion, within society. In large, complex societies, a system of legally established planning mechanisms is also required to make and implement the public policies that express societal goals and objectives. In many regions, such as the Vancouver—Seattle Corridor, planners have another obstacle to implementing their communities’ visions: jurisdictional boundaries that segment otherwise contiguous populations. In order to effectively implement the common goals and values of such a divided region, a planning system is needed that will transcend political boundaries. Many multijurisdictional regions have developed mechanisms in order to make and implement policies, and to provide advice to governing authorities. These mechanisms are considered as possible models for the components of a transborder planning system for sustainable development in the Vancouver—Seattle Corridor. A theoretical planning system is constructed and presented to a set of targeted individuals for evaluation. Many conflicts emerge between the theoretical planning system and the political realities of the study region. The goal of sustainability requires the resolution of such conflicts in the Vancouver—Seattle Corridor and in other planning regions.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.936

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 studies0.0010.000
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 head0.013
GPT teacher head0.244
Teacher spread0.231 · 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 designOther design
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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

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