Having Your Porn and Condemning it Too: A Case Study of a “Kiddie Porn” Expose
Bibliographic record
Abstract
In 2005, the Toronto Police Department’s Sex Crime Unit embarked upon the unprecedented move to go public with forensic evidence related to an on-going child pornography investigation. This strategy provided the public with exceptional glimpses into the taboo arena of child pornography. In this article, I trace the media coverage of this investigation to highlight the rhetorical and aesthetic components that, I posit, are related to a pedophilic logic. My goal is to reveal the latent but omnipresent desire encoded in the media narratives to imagine children and childhood in sexualized contexts. In particular, my analysis maps the literary and photographic aspects of the coverage to highlight the “performative contradiction” of the texts; though the media articulated a one-dimensional story of outrage and condemnation, the rhetorical and pictorial aspects of the story produced meanings that undermined the purported censure of child sexualization.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".