MétaCan
Menu
Back to cohort
Record W4379537166 · doi:10.5539/jpl.v16n3p10

Personal Data Protection in the Iranian Legal System

2023· article· en· W4379537166 on OpenAlexvenueno aff
Mohammad Mustafa Mohiqi

Bibliographic record

VenueJournal of Politics and Law · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsData Protection Act 1998Personally identifiable informationOrder (exchange)LawLegal liabilityInformation privacyCyberspaceInformation privacy lawRight to privacyPrivacy laws of the United StatesBusinessPolitical scienceInternet privacyPrivacy lawLiabilityPrivacy policyThe InternetComputer science

Abstract

fetched live from OpenAlex

Currently, legislators are paying special attention to the personal data of individuals since these data can be processed, transferred quickly and are available in cyberspace. The purpose of this article is to describe the process by which Iran's legal system protects personal information and privacy. There is no specific law in Iran regarding the protection of personal data, and therefore this data should be protected in accordance with other laws. While there is no specific legal sanction in the Iranian legal system for the violation of data privacy, it is not without legal consequences, and for the legal consequences, one can refer to other Iranian laws and foundations. For example, for civil remedies, it is possible to make reference to the Civil Liability Act. Based on the different laws of Iran, it can be seen that in this country, the principle is to safeguard the privacy of the individual. Although the right to privacy may not be violated in all cases, it may be violated in exceptional circumstances, such as when it comes to national security, because in every country, issues such as order and public interest take priority over the rights of individuals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.073
GPT teacher head0.329
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Politics and LawSame topicPrivacy, Security, and Data ProtectionFrench-language works237,207