The Crime of Attacking Private Life in the System of Combating Saudi Informatics Crimes
Bibliographic record
Abstract
The Internet has become the scene of many crimes that are completely different from traditional crimes, including crimes against individuals' privacy and privacy, by accessing personal information and data that they maintain using the electronic means and media of the network, ranging from defamation to extortion, Theft and destruction of information, and may even be used to impersonate persons and commit crimes in their name, and so on. Private life is one of the most important concepts that fall within the scope of human rights enshrined in all international covenants and covenants, which are committed to protecting national laws and legislations in all countries. However, technological changes have posed many challenges to the criminal protection of private life in the Internet environment.This research aims to identify the criminal protection of private life through the Internet in Western and Arabic laws, as a basis for research into the crime of attacking private life in the system of combating Saudi informatics crimes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".