Dealing with globalisation at the regional and local level: the case of contemporary containerization
Bibliographic record
Abstract
This paper considers the challenges that ocean container companies face in developing global operations in the ocean and land realms. The recent horizontal and vertical integration of the ocean carriers and the subsequent formation of global networks have taken place for the transport of goods at sea. Conformity in operations is to be expected in the globalisation process; however, there is less conformity of operations on land. The cooperation strategies at sea appear to break down when the goods reach the port. Each shipping company has its own network of agents and agreements with shippers, freight forwarders or land transportation companies to handle goods on land. Each also has varying logistics provision abilities. Moreover, the land areas vary according to simple geography, economic development, transport infrastructure and institutional constraints. Of these differences, we suggest that institutional constraints create the greatest challenge to the ocean carriers adopting a universal land strategy to service their customers. The paper focuses on three major areas for containerisation—East Asia, Northwest Europe and North America—and draws on interviews conducted with shipping industry executives in Norfolk (Va.), Rotterdam, Le Havre, Hong Kong and Singapore. The paper ends with a look at the land operations of Maersk Sealand as an example of a company with varying capabilities and strategies for the landward transport of containers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".