LES STRUCTURES DE GOUVERNANCES DE LA CHAINE TRANSACTIONNELLE DU TRANSPORT FLUVIAL DE CONTENEURS SUR LE RHONE
Bibliographic record
Abstract
The modal share of container barging in French maritime ports (9% of TEU in Le Havre and 5% in Marseille in 2007) is significantly lower than elsewhere (32% in Rotterdam and 33% in Antwerp). Some reports and studies explain the viscosity of container barging flows as a result of several factors, generally concentrated around the seaport community. In continuation of previous seminal works, this paper adopts a neo-institutional approach (Williamson, 1985; 1996) of container barging to understand how the factors generating this viscosity are managed in the Rhône-Saône river basin. Section 1 describes the characteristics of the transaction of container barge transport. Section 2 deals with observed governance structures (Logirhône and RSC) of this transaction chain in the Rhône-Saône river basin and shows how vertical integration helps to control it. . Section 3 is devoted to the transaction' attributes (asset specificity, frequency, uncertainty) of this chain. It confirms Williamson (1996) remediableness criterion, i.e. that the observed governance structure of a given transaction is presumed efficient and aligned to its attributes. Finally, it shows that the development of container barge transport on the Rhône-Saône basin in France is not impeded by its degree of integration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".