The emotion impact on and coping strategies employed by police teams investigating Internet child exploitation
Bibliographic record
Abstract
Along with its benefits, Internet access has a very dark side. The privacy and anonymity afforded by the Internet has created a vast repository for col-lectors and preferential child molesters seeking to prey on the most vulnerable members of society. The availability and affordability of digital cameras, videos, and webcams has opened the doors to the production and online trading of graphic child abuse images. Technological advances have facilitated the increased availability of child pornography in Canada and internationally and contributed to the development of advertising for child sex tourism des-tinations (Criminal Intelligence Service Canada [CISC], 2005). As a result, criminal networks have emerged online to share child pornography, learn how to produce child pornography, and exchange tips on how to avoid detection (CISC, 2005). In the mid-1990s police agencies across the globe began to recognize that the Internet was being used as a tool to facilitate child exploitation. In response, the first investigative units dedicated to identifying and locating victims and suspects of Internet child exploitation (ICE) were created. Since that time, ICE units have emerged internationally as police agencies respond to the increasing volume of tips and requests for assistance. According to the Virtual Globe Task Force, a partnership comprising the Access to the World Wide Web has become areality for millions of people in our societyduring the past decade. Recognition of the value of advancing technologies has rapidly made the Internet an indispensable tool for use in busi-ness, education, and personal and family communi-cation. In Canada, there is at least one regular Internet user in 64 % of households, accessing the Internet from the home, school, public library, or other location (Statistics Canada, 2003). The Internet is fuelled by the wealth of easily available information, offering global searches on every topic imaginable; it provides instant communication and opportunities to plan and book vacations and pur-chase automobiles and other goods and services at the stroke of a key. Access to the Internet has forever changed the world, creating a truly global society
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".