Criminal Protection of Privacy in the Jordanian Cybercrime Law No. 27 of 2015
Bibliographic record
Abstract
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.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".