Bibliographic record
Abstract
This article refers to bibliographical research with the objective of analyzing the possible aspects of cybercrimes, detailing their variations and how they are practiced. With the advancement of technology, there has been a parallel increase in digital crimes, which are considered any and all infractions that involve the use of electronic tools or the internet itself, violating legislation and/or data privacy. To date, there are more than 6 (six) spe-cific laws against digital crimes that guide our legislation, causing penalties ranging from 4 (four) months to 8 (eight) years of imprisonment and fines for those who violate them, which were created and established throughout the events and many of them even titled with the names of the victims, as well as the laws already established in the Penal and Civil Code that are used by analogy to resolve some cases. Brazil was elected the 5th (fifth) country that suffers most from virtual crimes and scams, representing around 9.1 million occurrences, counting only the first quarter of 2022, in addition to which we also stand out due to data leaks, According to the Dutchonline security organization Surf-shark, we occupy 6th (sixth) place in the ranking of destinations with the highest inci-dence of this type of activity.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.024 |
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".