Virtual Criminality: Examining the Successes and Pitfalls of the Fight against Sexual Abuse of Children on the Internet through a Select American, European and African Nations
Bibliographic record
Abstract
The world has become a global village where activities hosted in one jurisdiction may be accessed by a visitor on the internet site from another jurisdiction from where it is hosted. However, one of the downsides of this technological advancement is its adoption for disseminating information and pictures that are inimical in respect for children, namely sexual abuse of children of which child pornography is a key element. This is an online business where perpetrators make humongous profits at the detriment of the children depicted in the pornographic materials. The key objective of this paper therefore is to interrogate the efforts of the United States, Canada, United Kingdom, Republic of Ireland, Ghana and Nigeria as well as the international efforts made so far at curbing the rise in such online activities and to find out the success or otherwise of such efforts. The paper adopts the doctrinal methodology to discover the efforts made so far and the factors, if any, militating against the success of the efforts. The study finds that there have been significant interventions through regional conventions and domestic legislation to arrest the scourge but, the impacts of these efforts have been greatly eroded by jurisdictional issues and absence of uniformity in the definition and scope of child sexual abuse. The paper finally recommends the synergy of enforcement mechanisms among nations where the crime is being perpetrated. It also recommends a comprehensive national campaign as well as parental control of their children’s activities on the internet.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".