Postdigital Bodies: Young People’s Experiences of Algorithmic, Tech-Facilitated Body Shaming and Image-Based Sexual Abuse during and after the COVID-19 Pandemic in England
Bibliographic record
Abstract
In this paper, we draw upon a study exploring how COVID-19 and social isolation impacted young people’s (aged 13–18) experiences of online sexual and gendered risks and harms in England during nationwide lockdowns and upon their return to school. We explore the complexities, tensions and ambiguities in youth navigating algorithmised feeds on social media apps such as TikTok and content featuring idealised cis-gendered, heterosexualised feminine and masculine embodiment. Young people repeatedly witness hateful and abusive comments that are algorithmically boosted. We argue that this toxic content normalises online hate in the form of body shaming and sexual shaming, developing the concept of the postdigital to analyse the offline, affective, embodied and material dimensions of online harm, harassment and abuse. We also explore young people’s direct experiences of receiving harmful comments, including girls’ and gender and sexuality-diverse youth’s experiences of body and sexual shaming, as well as boys’ experiences of fat shaming; which, in many instances, we argue must be classified as forms of image-based abuse. Using our postdigital lens, we argue that the ways heteronormative, cis-gendered masculine and feminine embodiment are policed online shapes behaviour and norms in young people’s everyday lives, including in and around school, and that better understanding and support around these issues is urgently needed.
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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 | 0.001 | 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 | 0.000 | 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".