Notice bibliographique
Résumé
Online child pornography is an unintended aspect of the widespread adoption of information and communications technologies. Child pornography involves the sexual abuse of children on a global basis. It appears that a once limited trade has seen remarkable growth, with the potential to intrude into the homes and workplaces of all those connected to the internet. Occasionally, exposure to this form of pornography may be unintended, but in most cases it is deliberately sought out, retained and traded worldwide. There have been many instances of law enforcement action, both locally and internationally, targeting those involved in the possession or distribution of online child pornography, some of which have involved thousands of suspects. This paper looks at 31 well-publicised operations and considers the law enforcement implications of these for future operations. It starts to fill a significant gap in our understanding of online child pornography. Toni Makkai Director The amount of media attention given to the issue of child pornography has risen dramatically. A search of a global English-language newspaper archive showed that between 1976 and 1989 there were 2,095 articles that referred to child pornography, between 1990 and 1994 there were 4,573 articles, between 1995 and 1999 there were 21,507 articles and between 2000 and September 2004 there were 51 ,270 articles (Factiva 2004). To some extent, the results of the search are affected by changes to database recording practices overtime. The dramatic increase in media coverage also reflects a number of other developments: the proliferation of material through the use of digital information and communications technologies; the introduction in many countries of specific offences of possessing child pornography; increased police activity in response to new laws; and a fascination with the aspects of international networking and the numbers of persons involved. There has been a steady stream of reports of various police operations that have led to the identification of tens, hundreds and even hundreds of thousands of possible suspects, involving a confusing array of individuals, networks and police operation code-names. This paper analyses major operations that have been reported since the early 1990s following the advent of the internet and the widespread enactment of child pornography possession offences. Methodology The following English-language sources were searched for reports of police operations against online child pornography: * the Factiva database of English-language newspapers from 1976 to 2004; * an Australian media digesting service, a Google media alert service, the Cybercrime-alerts service and the Computer Crime Research Center alert service (to collect reports in the period from August 2003 to September 2004); and * governmental and non-governmental agency reports on the policing of online child pornography. Because this research was based on English-language searches, the material derived is principally from Australian, Canadian, United States and United Kingdom sources. The research does not seek to provide a comprehensive global survey or a representative sample of police operations against online child pornography. This study provides a basis for analysis at least for those police operations that have been reported on. The reports considered are not exhaustive, Media reports must be treated with caution, as they may be incomplete or misleading. The details of the matters considered here are therefore drawn from a variety of sources wherever possible. Information, such as the number of persons involved as suspects, persons arrested and persons convicted, should be treated as indicative only. Other limitations of the material presented are: many networks are international and span differing laws against child pornography; media attention is directed to major cases; and there are too many police stings to catalogue here. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».