The role of seaport-based logistic platforms in the automotive supply chain: The Lower Seine Case
Bibliographic record
Abstract
This study is an attempt to understand the role of seaport-based logistic platforms in the distribution of automotive parts from France to assembly plants in emerging countries. A diverse range of opinions on location and sourcing strategies of automakers and part-suppliers have been presented in economic geography: namely the ways in which part suppliers follow or not auto-makers to new markets and how sourcing logistics takes place. In response to this, the authors clarify the reality of such logistics by considering the case example of a third-party logistics (3PL) provider (Gefco) which has developed auto-parts logistic-platform to supply overseas car assembly plants (Brazil, Argentina, China) for PSA Peugeot Citroën. The authors will shed light on the fact that, in order to accommodate distribution to overseas assembly plants, logistic platforms operated by 3PL providers are located near seaports, and will seek to further clarify the functions of these platforms. This analysis will also enable new insights to be gained: the follow sourcing strategy is rather limited to few components and instead part-suppliers tend to ship most of auto-parts from Western Europe. How can we explain the actors supply chain reacts facing this competitive reversal of logistical flows? In this context, 3PL logistics platforms play an important role, ensuring high frequency deliveries to overseas assembly plants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".