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Record W589157143

Governance in logistics platforms: an analysis of the elements and attributes to be considered in this type of enterprise logistics

2013· article· en· W589157143 on OpenAlexaboutno aff
Rafael Mozart da Silva, Eliana Terezinha Pereira Senna, Luiz Afonso dos Santos Senna, Orlando Fontes Lima

Bibliographic record

VenueJournal of Transport Literature · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessHierarchyCorporate governanceDistribution (mathematics)Industrial organizationProduction (economics)Integrated logistics supportTraffic managementProcess managementTransport engineeringEngineeringEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

The Logistics Platforms are logistics enterprises of large scale that serve an area or region, where they concentrate and realize activities related to production, transport, logistics and goods distribution, using the same basis of services where different workers involved are related to goal of achieving greater efficiency in their operations. This research aimed to identify the attributes and elements that can be applied to the governance of logistics platforms. To reach the purpose of this research, the authors performed a literature review of the main concepts related to governance and logistics platforms and also a survey of features of logistics platforms located in Europe extracted from the reports Feasibility of Freight Villages in the NYMTC Region and An Exploration of the Freight Village Concept and Its Applicability to Ontario. As a result it was found that the governance of this type of enterprise is associated with influence of power, the degree of hierarchy between the different workers involved or participating, and also the levels and synergies that are established in interorganizational relationships both in the public and private spheres.

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)0.000
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 score0.004
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.240
Teacher spread0.218 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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