Protection of Privacy and Personal Information: A Comparative Study between Iranian Law and Canadian Law
Bibliographic record
Abstract
The right to protection of privacy and personal information is one of the rights and freedoms that its aim is to protect the rights of individuals and respect to fundamental human rights. Identification the right to privacy and personal information and protecting it in law and determining the legal and criminal sanction, especially considering that this area is full of many problems can be challenging. The purpose of this article is to examine the protection of privacy and personal information and for understanding the laws in this area better; a comparative study has been conducted. The question of this article is what is the legal framework for the protection of privacy and personal information in Iranian and Canadian law? And Has Iranian law developed in line with other countries? In This research has studied the relevant sources and collected information by descriptive-analytical method After examining the concept of privacy, the protection of privacy and personal information in Canadian and Iranian law will be examined in detail, and finally it will be stated that a comprehensive and unified law is required in Iranian law. It is worthy, as progresses that accrue on this issue in other countries law in recent years, a single and comprehensive law should be formulated in Iranian law and legal gaps in this issue should be eliminated.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".