The Legal Framework of Electronic Data Crimes
Bibliographic record
Abstract
In order to determine the legal framework of these crimes, we should distinguish between two types of crimes or attack electronic data, the first type when the technology of electronic data process and telecommunication have used in remote to commit crimes. In other words these crimes are committed through computer and the criminal description of these businesses belongs to the known of types of traditional crimes like theft, fraud and other crimes. This type of crimes call un informatics in the global information network “ internet” also this field includes the crimes of usage of “internet” and electronic data processes tools to show the pornographic images or diffusion a messages which are inciting to racism, racial, religious, discrimination or exposure to the personal liberty or intellectual property. The second types of electronic data process crimes when the technology of the electronic data and telecommunications are on remote and make it as a means of this crimes and their purpose too. And now we are in front of anew criminal acts which associated mostly to the exposure of security and integrity of electronic data systems and the confidentiality of the data and information that consist on. And this type of criminal information network called “internet”, this is done in the case of illegal entry in to these systems and exposure to it or to the information that contain it. So this research will base on the electronic data crimes which are connected to the internet, when it becomes a direct target and a goal in their contents, and regardless on the impulsive of behind of committing.
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 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".