Ways to compensate for the loss of privacy in the laws of Iran and Canada
Bibliographic record
Abstract
In the laws of Iran and Canada, any assault on the body, property, communications with their types and types, information and secrets, dignity and reputation, privacy and privacy, opinions and thoughts, writing, by natural and legal persons is prohibited and violation of privacy. It is considered private. In Iranian laws, cases of privacy violations are widely criminalized and both disciplinary and criminal punishments are considered for it; Also, the victim is entitled to compensation due to civil liability. The methods of compensation for the violation of privacy in Iranian law have been widely seen; Although it is mostly aimed at compensating the material damage of individuals. In any case, methods including compensation for material and moral damage, restoration of dignity, the obligation to apologize as compensation for the loss of privacy are foreseen. In the Canadian legal system, compensations such as compensation, ransom, apology and other cases of compensation are available without being formulated in specific laws and limited to specific criteria, with the opinion of the hearing authority. For example, the Human Rights Court or any of the normal courts can consider the best and most complete compensation for the victim depending on the specific case and conditions of each case. In this article, we analyze the methods of compensation for the loss of privacy in Iranian and Canadian laws using a descriptive analytical method.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".