L’avènement de corridors ferroviaires trans-asiatiques : quel levier de développement ?
Bibliographic record
Abstract
Depuis 2011, on observe une diversification de liaisons ferroviaires de transport de fret entre la Chine et l'Europe. Cette initiative commerciale s'est rapidement développée : le nombre de trains a connu une augmentation rapide, et les ambitions des partenaires chinois comme européens sont élevées. Certes, le rail dispose d'une part de marché du fret transasiatique encore faible. Les projets de développement de liaisons ferroviaires pour le fret transasiatiques ont cependant connu une relance spectaculaire avec l'avènement du projet Belt and Road Initiative. On assiste à une expansion des volumes, des services proposés, le tout accompagné par la définition et la mise en œuvre de nouvelles infrastructures ferroviaires, en Russie, en Chine, vers l'Asie du Sud-Est, l'Iran ou le Pakistan, dans ce que la Chine présente comme des corridors de développement. Tout à la fois leviers potentiels de développement et actifs de rapprochement stratégiques, comment ces projets de corridors se développent-ils?
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".