The snitch factor: the harms associated with disclosing and reporting technology-facilitated sexual violence in schools
Bibliographic record
Abstract
Part of a larger study examining teacher, administrator, and youth perspectives on technology-facilitated sexual violence (TFSV) in secondary schools, this article focuses specifically on the harms associated with disclosing and reporting digital sexual harms. Critical discourse analysis was used to analyze semi-structured interviews with sixteen young people and thirteen teachers and administrators. When asked about barriers to seeking and accessing help in response to TFSV, students and staff spoke to the ‘the snitch factor,’ encompassing four sub-discourses: snitch as next target of TFSV, snitching as futile, snitch as troublemaker, and snitch as complicit in harm. Labels like ‘complicit’ and ‘troublemaker’ are not simply descriptors but carry powerful social and institutional meanings. Findings reveal that for both victims and bystanders, the snitch factor is a meaningful barrier to seeking help and often leads to further gendered and racial violence, contributing to school spaces that encourage silence rather than prosocial intervention.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".