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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".