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Record W568151749

Innovation and Stakeholder Collaboration in West Coast Gateways: An Analysis of Seaport and Freight Movement Industries

2010· article· en· W568151749 on OpenAlexaboutno aff
Clarence Woudsma, Peter Hall, Thomas Joseph O'Brien

Bibliographic record

VenueTransportation Research Board 89th Annual MeetingTransportation Research Board · 2010
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsStakeholderIncentivePort (circuit theory)BusinessSupply chainIdentification (biology)Stakeholder engagementRegional scienceTechnology transferIndustrial organizationMarketingPolitical sciencePublic relationsGeographyEconomicsEngineeringInternational trade
DOInot available

Abstract

fetched live from OpenAlex

How do organizations and stakeholder dynamics contribute to the adoption of innovation in gateways? We explore this question through a comparative assessment of the situation in two of North America’s major West Coast Gateways – the port of Vancouver, British Columbia, and the twin ports of Los Angeles/ Long Beach (LA/LB), California. Our research approach consists of the identification and assessment of exemplary innovations drawn from the areas of policy, technology, and operations with an emphasis on those directly related to environmental considerations. The case studies of innovation each illustrate the diverse sources of demand for and supply of innovation. These sources may be highly localized or may be regional or even pan-coastal in their origin. There are examples of innovations promoted by lead firms or by lead jurisdictions. What is common to all these innovations is the important role played by the mediators of innovation, both in their initial development and especially in their diffusion and adoption. Each of the following mediators of innovation can be found, typically in combination: information supports, stakeholder forums and engagement, technology transfer, adoption incentives and regulation, and formal and informal institutional arrangements.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.064
GPT teacher head0.344
Teacher spread0.281 · 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.

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

Citations2
Published2010
Admission routes1
Has abstractyes

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