Bibliographic record
Abstract
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. …
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".