The Irony of Charging Children with Distribution of Child Pornography
Bibliographic record
Abstract
Those who are adjudicated or convicted of child pornography offenses are sexual offenders and often predators…Teenagers who engage in sexting should not face the same legal and moral conundrum . (Judge Robert L. Steinberg at p.11 in RE: C.S ., 2012) The statute at issue was designed to protect children, but in this case the court has allowed the state to use it against a child in a way that criminalizes conduct that is protected by constitutional right of privacy . (Judge Padovano, dissenting at para 241 in A.H. v. State of Florida , 2007) Introduction The previous chapter addressed the difficult issues of sexism and misogyny as they are ingrained in contemporary society, or, as they have resurfaced with a vengeance through the uses of digital and social media, which enable (but do not cause) rapid proliferation and perceived anonymity. Chapter 2 also highlighted the perspectives of the participants in the DTL Research that reflected their confusion about the difference between expressions for “fun” and expressions that were intentionally harmful as they thought about how they define the lines between joking or teasing and criminal harassment, threats, and distribution of intimate images. I also presented research findings and theories on moral development, moral disengagement, and empathy that have been conducted in the field of scholarship on bullying. The DTL Research, as analyzed within those theories, confirms developmental differences among children as they grow up, which sometimes prevents them from making thoughtful ethical and empathetic decisions. As I explained in Chapter 2, Digitally Empowered Kids (DE Kids) are influenced by a context in which adult society sends very confusing messages about sex and sexuality, freedom of expression, and privacy through popular culture, news, and film media. Children and teens witness violent models of verbal and physical behavior and communication often perpetrated by adults. It is no wonder kids have difficulty defining the lines between jokes and potentially criminal offenses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".