Bibliographic record
Abstract
This Open Access book explores how teens use social media, how they produce, consume, and share sexual images, and how they understand and respond to harmful digital sexual content and interactions. Capturing the views of nearly 500 young people across the UK our book shows how image-based sexual harassment and abuse (IBSHA) impacts all young people and is a society wide problem that needs to be urgently addressed.Developing a socio-cultural and tech affordances approach to understanding social media platform economies, we show how game-like engagement features keep users on apps and expanding their networks, opening up teens to considerable online risk and harms. We argue a lack of consent in the digital environments intersects with society-wide, age old norms of gender and sexual inequalities, facilitating image-based sexual harassment and abuse (IBSHA). Educational policy and curriculum focused on abstinence anti-sexing messaging and a focus on child pornography laws, fail to address gendered and sexualised power dynamics and peer on peer abuse. We argue a multifaceted approach is needed to improve the law, technology companies and education. Better digital literacy and sex education that covers social media use, risk, harms and reporting in platform specific ways would offer better supports for youth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".