Bibliographic record
Abstract
Crime is an action or omission, which constitutes an offence and is punishable by law.A crime is an offence that merits community condemnation and punishment, usually by way of fine or imprisonment.Crime takes place when a person deliberately practices deception in order to gain something unlawfully or unfairly.While crime is most commonly committed to obtain benefits of value, it sometimes occurs solely for deceiving another person or entity.Computer crime alternatively referred to as cybercrime, e-crime, electronic crime, or hi-tech crime is an act performed by a knowledgeable computer user, sometimes referred to as a hacker that illegally browses or steals a company's or individuals private information.In some cases, this person or group of individuals may be malicious and destroy or otherwise corrupt the computer or data files.Cybercrime may threaten a person or a nation's security and financial health.Issues surrounding these types of crimes have become high profile, particularly those surrounding hacking, copyright infringement, unwarranted mass-surveillance, extortion, child pornography, and child grooming.This paper focused on the causes, types, detection and prevention of computer crime.The paper also reviews the various forms and types of computer crime practice, their impact and recommendations that will curtail this bad menace.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.113 | 0.045 |
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".