“They’re flooding the internet”: A cross-national analysis of \nnewspaper representations of the ‘internet predator’ in Australia, Canada, the UK and USA
Bibliographic record
Abstract
Although online child sexual abuse is an issue of international concern, little is known \nabout the news media’s role in its construction. In this study I draw upon a corpus of \n6,077 newspaper articles from Australia, Canada, the UK and USA – four member \ncountries of the Virtual Global Taskforce set up in 2003 to combat online child abuse. \nThrough a quantitative content analysis, I trace the trajectory of news coverage in \neach country and identify the news hooks and key events through which the issue has \nbeen framed during peak periods. This is complemented by a critical discourse \nanalysis, through which I interrogate discourses around spatiality, particularly those \npertaining to the paedophile’s migration from the ‘real world’ to ‘cyberspace’, and \nfrom the ‘outside’ into the heart of ‘the home’. The quantitative element of my study \nshows that: (1) although coverage began to emerge during the mid-1990s, it only \nbegan to accelerate after the turn of the century; (2) online abuse has been defined \nthrough episodic coverage, often around high-profile ‘grooming’ cases; (3) coverage in each country has largely followed a unique, nationally-specific narrative (shaped \nby its own socio-political context); and (4) although coverage has gone through \nperiods of peaks and troughs, there are few signs that online abuse is slipping off the \nnews agenda. In my qualitative analysis, I present evidence that: (1) claims-makers \nhave drawn upon existing understandings of, and fears about, parks and playgrounds \nto construct aspects of the internet as online ‘paedophile places’; (2) a discourse of \ntemporal proximity has been adopted to depict children as being ‘seconds’ or ‘clicks’ \naway from an internet predator; (3) this discourse of temporal proximity has been \nused to localise a global problem and depict the internet predator as being even closer \nto children than the traditional figure of ‘the paedophile’; and (4) the internet has been \nframed as bringing fundamental changes to how sexual threats to children should be \nunderstood. Through this analysis I argue that these discourses have been used to \nlegitimise tighter regulation of children’s lives and, although specific to the internet, \nthey perpetuate myths about paedophiles, childhood, the family and home that limit thinking about child sexual abuse on a much broader scale.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".