The Research to Remove Barriers Between China and Tunisia in the Shipping Field
Bibliographic record
Abstract
Over the years, the trade from China to Mediterranean countries has boomed spectacularly. The fast development of foreign trade has not only pushed China’s port industry and international shipping industry forward considerably, but also provided foreign ocean carriers and terminals opportunities and challengers. As Tunisia is a strategic location, the Tunisian ports overall goals are to increase the number of calls made to it. Then the market study between Mainland China and Tunisia turned to be necessary and meaningful.The objective of this research project is as a first a theoretical review to introduce and describe collaborative logistics management and investigate its consequence on the supply chain. This purpose necessitates a framework to support the collaboration between the entities in the chain especially in terms of logistics activities. Despite the identified needs and potential benefits, there are still barriers, which must be identified to attain desired benefits.On a second part the purpose of this study is to analyze China’s port industry and ocean shipping market in a relation to trade with Tunisia and to recommend some suitable Chinese ports to cooperate with Tunisian ports and find the reason why some top shipping companies are still not carrying goods to Tunisia. Two market surveys are performed, one for finding suitable Chinese ports for the Tunisian ports to cooperate with, and one to collect information from several top shipping companies using the Tunisian ports.In the third and last part a small research study focuses in the costs and quality shipment from China to Tunisia showing Tunisia as a transshipment port for the whole Maghreb. The case of study will be a proof of future consideration for Chinese shipping companies to assure a direct shipping line to the port of Tunisia. The case of study will be affirmed by statistical and theoretical analysis in order to confirm the idea.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".