Multimodal carrier liability in the U.S. and Canada : towards uniformity of applicable rules?
Bibliographic record
Abstract
From its inception, intermodal transport of goods has served trade, shippers and carriers, radically increasing transactions of goods worldwide. Multimodal carrier liability rules, however, have not evolved with the same rhythm and remain fragmented cross-modally and cross-country. This is also the case of the U.S. and Canada. The need to seek uniformity of applicable rules in these two countries led us to the comparative analysis of unimodal (landocean) rules in these two countries. Guided by past failed initiatives (1980 United Nations Convention on International Multimodal Transport), the European intermodal reality, transport deregulation, pragmatism, fairness in the relation between the carrier and the shipper and Law & Economics principles, we used harmonization, codification and contractualism in advancing our suggestions on uniform multimodal carrier liability rules.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".