L'intervention sur terre des armements de lignes régulières : le cas de la rangée Nord Europe.
Bibliographic record
Abstract
Shipping lines are maritime carriers who on occasion implement handling terminals, hinterland transport services and inland terminals in order to improve their competitiveness. These courses of action are developed in different ways but every shipping line hopes to develop a competitive advantage on the market invested. The thesis aims to answer two questions: what are the determining factors for the different forms taken by the enlargement of scopes of shipping lines? Do these differences have an impact on the competitive advantage of the shipping lines? A theoretical framework mixing concepts in proximity economics and strategic management has been built and tested on the North Range. The reasons why Maersk, MSC, CMA CGM and NYK implement different port and hinterland services, compared to other carriers, have been analysed. The competitiveness triggered by these enlargements of scopes has been analysed. Besides the industry-firm-territory logic, competitive dynamics between shipping lines on one hand, and between territories (port and its hinterland) on the other hand, impact the way carriers implement their port hinterland services. The competitive advantage triggered depends on the temporal dynamic of the services implemented. The sustainability of these advantages depends on the density and connexity of port hinterland services developed
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".