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<i>MUTUAL SPECIALISATION, SEAPORTS AND THE GEOGRAPHY OF AUTOMOBILE IMPORTS</i>

2004· article· en· W1484959749 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTijdschrift voor Economische en Sociale Geografie · 2004
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsUniversity of Waterloo
FundersUniversity of California Transportation CenterNational Science Foundation
KeywordsPort (circuit theory)Space (punctuation)Economic geographyProcess (computing)Perspective (graphical)BusinessIndustrial organizationInternational tradeRegional scienceTransport engineeringEconomicsGeographyComputer scienceEngineering

Abstract

fetched live from OpenAlex

ABSTRACT This paper argues for a more actor‐centred approach in freight transportation studies, one that includes freight shippers and public authorities, as well as carriers, and that pays close attention to the relationships between these actors. One advantage of this approach is that it focuses on the conditions under which global logistics flows may become relatively fixed in particular localities. The perspective is illustrated through a discussion of the geography of port usage by importers of automobiles to the US since 1980. The need to secure space at or near marine terminals for vehicle processing activities is a driving factor in port selection. While the overall trade in automobiles has not become concentrated in fewer ports over the last 20 years, individual firms are concentrating the bulk of their import operations in fewer ports. This mutual specialisation involves a process of interpenetration between actors that is only visible in a disaggregated analysis.

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.

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 score0.643
Threshold uncertainty score0.889

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 head0.003
GPT teacher head0.175
Teacher spread0.172 · 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