Comparative Study of Compensation for Breach of Technology Transfer Contracts in the Legal Systems of Iran, Canada, and France
Bibliographic record
Abstract
Technology transfer contracts are among the common agreements in the field of industrial property law. Based on these contracts, specific technologies, often of strategic value, are made available by the holder to the transferee in exchange for a specified amount and for a defined period. However, like any other contract, these agreements may be breached. Each legal system may adopt different approaches to compensating for the damages incurred in this regard. This study examines these approaches in the legal systems of Iran, Canada, France, and Islamic jurisprudence. In the Canadian legal system, several documents, including the Public Servants Inventions Act and guidelines on intellectual property management issues, have been enacted regarding technology transfer contracts. In the French legal system, Law No. 75-1334 and Article 442-1 of the Commercial Code govern this area. Meanwhile, in the Iranian legal system, laws such as the Electronic Commerce Law and the Foreign Investment Promotion and Protection Act are relevant. Additionally, Islamic jurisprudence has imposed specific restrictions on the transfer of intangible assets. The primary question addressed in this research is: How is compensation for the breach of technology transfer contracts determined in the legal systems of Iran, Canada, France, and Islamic jurisprudence? The findings of this descriptive-analytical study indicate that the foundations and methods of compensating for breach of technology transfer contracts differ fundamentally among the legal systems of Iran, Canada, France, and Islamic jurisprudence. Furthermore, France and Canada, as transferring countries, always seek full compensation in this regard.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".