The SARS‐CoV‐2 Pandemic Crisis and the Evolution of Cybercrime in the United States and Canada
Bibliographic record
Abstract
The appearance and transmission of the SARS-CoV-2 virus causing the outbreak of Covid-19 in China and around the world between 2019 and 2022 has transformed living, working and interpersonal relationship habits. This chapter aims to study the impact of the SARS-CoV-2 pandemic on cybercrime in the United States and Canada, by exploring the following general research question: how did the SARS-CoV-2 pandemic and its consequences affect cybercrime in the United States and Canada? It draws a rapid portrait of the pandemic crisis in Canada and the United States and discusses societal changes, namely the acceleration of society cyberization. Targets, victims and malicious actors (cybercriminals, in other words) are introduced in the form of a literature review. Then, the impacts of the health crisis on the evolution of cybercrime in Canada and the United States are explored.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".