A Comparative Study on the Role of the Electronic Commerce Act in Remote Transactions and Its Effect on Compensation from Iran and France Legal Perspective
Bibliographic record
Abstract
Legal implications in various fields of e-commerce transactions are described by means of e-commerce from one of the parties and through the transaction implemented by Internet. Online contracts are a manifestation of true innovation in the field of traditional legal agreements. The main issue of concern is the lack of tools has traditionally been used to express the will of the contract.The lack of legal grounds to use in e-commerce, such as: Expert of judges, the admissibility of electronic documents, electronic signatures, the principle of good faith, law of consumer protection, commercial and competition law and how to compensate both material and spiritual is the most important challenge of the country's legislative system. The most important distinction between the Iranian and French law is on the implementation of its damage compensation that in French law is detailed discussions covering the damages due to breach of contract litigation is not compensable.But the other hand, moral damages, such as mental anxiety, loss of credibility and like that is compensable, while this is not done in Iran. Experience of law between Iran and France showed a weak pattern in consumer protection in e-commerce contracts. In this cross-sectional study to evaluate the role of trade in remote transactions and its effect on Iran and France in damage compensation from the legal perspective.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".