Bibliographic record
Abstract
La pandémie de SARS-CoV-2 a eu un impact indéniable sur la cybercriminalité. La crise initiale s’est transformée en une catastrophe mondiale aux conséquences multiples dans les domaines de l’économie, de la santé, de la politique et de la société. Évolutions du cybercrime durant la pandémie de Covid-19 explore la manière dont cette urgence mondiale a influencé la cybercriminalité, laquelle a augmenté et évolué, se nourrissant de nouvelles vulnérabilités.Le monde, déjà confronté à de nombreuses tensions, a vu les effets de la crise exacerber les problématiques de cybercriminalité. La radicalisation et l’usurpation d’identité ont trouvé un terrain propice à leur développement sur les réseaux. Les criminels ont pu adapter leurs modes opératoires, leurs cibles et leurs vecteurs d’attaque. La réponse des forces de l’ordre et des autorités publiques, en ce qui concerne l’aspect juridique, policier et politique de la cybercriminalité, a dû s’adapter afin de mieux lutter contre l’augmentation de ce phénomène.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".