Cryptomarchés et carding : impact sur l’offre et la demande
Bibliographic record
Abstract
Dans le présent article, il est question de décrire les activités marchandes des cardeurs sur les cryptomarchés au moyen d’un échantillon de 17 619 annonces de produits ou de services relatifs aucardingainsi que d’un second échantillon recensant les activités de 4 294 vendeurs. Notre démarche s’attache plus précisément à constater si l’avènement des cryptomarchés, comme lieu de convergence pour les cybercriminels, a eu un impact sur l’offre et la demande de tels produits et services. Pour mener à bien cet objectif, une typologie des différents produits et services est réalisée. Dans un premier temps, des analyses descriptives, mettant en lumière les proportions des différents types ainsi que la distribution des prix affichés par les annonces, permettent d’offrir un regard sur l’impact des technologies d’anonymat sur l’offre de produits et services. Dans un second temps, des analyses descriptives des transactions effectuées par les vendeurs sont réalisées pour examiner leur impact sur la demande de produits et services. Les principaux résultats indiquent que les cryptomarchés offrent tout le nécessaire pour commettre les trois étapes du script criminel ducarding. De plus, il est aussi montré que, comme dans le cas des vendeurs de drogue en ligne, le système d’évaluation formel a permis de transcender et d’améliorer les processus générateurs de confiance.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".