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Record W4416429053 · doi:10.1080/01425692.2025.2590616

The snitch factor: the harms associated with disclosing and reporting technology-facilitated sexual violence in schools

2025· article· en· W4416429053 on OpenAlexafffund
Salsabel Almanssori

Bibliographic record

VenueBritish Journal of Sociology of Education · 2025
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Windsor
FundersSocial Sciences and Humanities Research Council
KeywordsSexual violenceSexual abuseDomestic violenceHuman sexualityPoison controlSexual assaultHuman factors and ergonomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.334
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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