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Record W3109395641 · doi:10.5539/ass.v16n12p64

Criminal Protection of Privacy in the Jordanian Cybercrime Law No. 27 of 2015

2020· article· en· W3109395641 on OpenAlexvenueno aff
Wejdan Suleiman Irtaimeh

Bibliographic record

VenueAsian Social Science · 2020
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCybercrimeLegislatorLawCriminalizationCriminal lawCLARITYPolitical scienceLegislatureRight to privacyPrivacy policyInformation privacy lawData Protection Act 1998Privacy lawInformation privacyInternet privacyThe InternetLegislationComputer science

Abstract

fetched live from OpenAlex

This study deals with the issue of criminal protection of privacy in the Jordanian Cybercrime Law No. 27 of 2015, as the great developments in computer technologies and the widespread use of the Internet have led to the emergence of new forms of electronic crimes related to the protection of the privacy of individuals. The study indicated that the Jordanian legislator did not include in the Jordanian Constitution or in the Cybercrime Law any definition of the right to privacy that delineates its boundaries and clarifies its features. The study concluded that the Cybercrime Law was ambiguous in some of its articles, especially those related to the protection of the right to privacy. The Jordanian legislator did not include special provisions that explicitly criminalize assault on privacy, as it included provisions for other crimes that include assault on this right, which made it lose clarity, precision and accuracy of wording. Moreover, such provisions omitted other forms of electronic crimes related to the right to privacy, which constituted a legislative deficiency. The study concludes that there is a need to amend the Cybercrime Law No. 27 of 2017 and to have explicit provisions that stipulate the criminalization of assault on privacy, as well as the need to issue a special law to protect the personal information 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)0.001
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: none
Teacher disagreement score0.940
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.038
GPT teacher head0.290
Teacher spread0.252 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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