DE L’ALLOCATION À LA RESTAURATION DES RESSOURCES OU COMMENT UN CONTREMAÎTRE GÈRE LA FLEXIBILITÉ DU TRAVAIL
Bibliographic record
Abstract
In a port, the container handling task involves more than just transhipment. As the port combines the constraints encountered by the transport modes involved in the supply chain, the local managers in charge of controlling operations must mitigate these constraints and deal with the ensuing risks. Work flexibility is the only adjustment variable. When the operators are highly skilled and working in a small labour market as in this situation, the only way of reducing the effect of delays in the hierarchical production planning task and maintaining fluidity is internal flexibility. But is the frequent use of this kind of regulation free of consequences for the workers? Does it not threaten the long-term viability of the production system? How can we ensure the system is both robust and flexible? These are the questions that this article sets out to answer through an analysis of the supervision activity at an intermodal terminal. After an exploratory stage based on in-depth semi-structured interviews with several actors involved in the production process, and open observations at the terminal, systematic observations were conducted (by video, audio and written notes) of the foremen and the activities undertaken by the transport operators. When the foreman was available, clarification could be requested. The results focus on modes of flexibility and local control. They suggest that the local managers, in charge of mediating between management and production,crucially rely on organisational flexibility during peak periods and resource restoration during off-peak periods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".