Bibliographic record
Abstract
In recent years, the number of judicial practice cases of crimes against damaging computer information systems has climbed with the rapid development of science and technology.The controversy about how to apply this crime has become increasingly controversial.Especially in the context of a risky society, the protection line of criminal law is in advance, making the boundaries of this crime extended and more ambiguous.From the perspective of legal interests protected by this criminal provision and its implementation, this paper clarifies the boundaries of this crime and analyses the significance of this crime in practice in the context of traditional crimes or new cybercrime.This paper also describes the implicated relation of the perpetrator for committing multiple acts and clarifies the criteria for conviction when there is a "concurrence of crime" with traditional type crimes.As a result, over-expansion of this crime that would blur the boundary between this and the other crime could be avoided.In order to further clarify the scope of application of crimes against computer information systems, this paper also advocates combining relevant judicial practice cases, drawing on the German "shortened two-act offenders" theory and advocating the unity principle of subjectivity and objectivity.These approaches can improve the efficiency of judicial application and provide powerful help for regulating and preventing cybercrime.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".