What Is Self-exploitation? Rethinking the Relationship between Sexualization and ‘Sexting’ in Law and Order Times
Bibliographic record
Abstract
Since the early 1990s ‘sexualization’ has emerged as a ‘social problem’ whereby children, particularly white, heterosexual, middle-class girls, are purportedly being mal-socialized to deny their natural ‘innocence’, to prematurely embrace and express the characteristics of adult sexuality and to engage in ‘self-sexualization’ (APA, 2007; Smith & Attwood, 2011; Duschinsky, 2013a; Egan, 2013). In and around the same time as the public opprobrium about sexualization reached its pinnacle in the US and the UK, between 2006 and 2011 (Egan, 2013: 3–4), the West was also witnessing the rise of another representational practice, that of the sexy ‘selfie’ — semi-nude and sexually explicit self-portraits, taken at arm’s length or in a mirror, using a cellphone or digital camera, and then posted to social networking sites such as Facebook, Instagram or Tumblr. Also referred to as ‘sexts’ by academics and those in the media, although not typically by youth themselves (see Karaian, 2012; Ringrose et al., 2012; Albury et al., 2013; Peskin et al., 2013; Strassberg et al., 2013), sexy selfies have met with a great deal of international attention, if not enthusiasm, by parents, pundits, legal scholars, childhood sexualization critics, child protection and policing agencies, many of whom cite an increasingly sexualized culture as a key cause of the practice (Hasinoff, 2014). The Canadian context is no exception. Members of the Canadian Senate have expressed concerns about the links between ‘the social realities that drive the hyper-sexualisation of girls in modern culture’ and the sexual exploitation of youth via the creation of child pornography (Jaffer & Brazeau, 2011: 4). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".