Bibliographic record
Abstract
The SARS-CoV-2 pandemic has had an undeniable impact on cybercrime. The initial crisis quickly became a global catastrophe with multiple consequences in economics, health, and political and social fields. This book explores how this global emergency has influenced cybercrime. Indeed, since feeding off new vulnerabilities, thanks to the effects of the pandemic crisis in various states around the world, cybercrime has increased and evolved.In 2020, the world was already dealing with numerous tensions and the effects of the global crisis have therefore only tended to exacerbate the issues that relate to cybercrime. For example, radicalization and identity theft has found an environment in which they thrive: the Internet. Criminals have been able to adapt their modus operandi, their targets and their attack vectors. However, on the plus side, the response of law enforcement and public authorities, in terms of the legal, policing and policy side of cybercrime, has also been adapted in order to better combat the increase in this phenomenon.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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".