RECLAIMING DIGITAL INTIMACY FOR YOUTH: PILOTING DIGITAL SEXUAL VIOLENCE WORKSHOPS FOR UNDER-18S DURING THE COVID-19 PANDEMIC IN ENGLAND AND IRELAND
Bibliographic record
Abstract
Online sexual abuse and violence have become an urgent global problem for women and girls, and in particular for poor women, women of colour and LGBTQ women (Ging and Siapera, 2019). Online sexual harassment and abuse is an especially urgent issue for young people, for whom digital spaces are key sites of communication, identity formation, self-expression and sexual interaction. The toxic dynamics that frequently underpin these complex entanglements thus pose a significant threat to ethical digital intimacy. This situation became substantially more extreme during COVID-19, with rates of online abuse and harassment rising as young people have been forced to spend more and more time online. During this period, usage of particular platforms (e.g. TikTok) dramatically increased. A substantial rise in screen time also impacted young people’s experiences and digital intimacies in important ways. This paper reports on the findings of a cross-national study conducted in England and Ireland, which explored pedagogical interventions into the continuum of online and offline sexual violence amongst young people in schools. In particular, we are interested in how different social media platforms are used to perpetuate different types of online abuse based on certain technological affordances, such as Snapchat quick adds, shout outs and streaks, and Instagram direct message and group chat features. We make a number of concrete recommendations, pointing in particular to urgent paradigm shifts in digital ethics and digital safety initiatives, with a particular focus on platform algorithims, policies and governance.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".