Terms of Silence: Weaknesses in Corporate and Law Enforcement Responses to Cyberviolence against Girls
Bibliographic record
Abstract
Girls do not need merely to be empowered with technological know-how in order to engage fully online. While girls use digital and social media for self-expression, activism, and identity experimentation, their engagement is too often interfered with by online gender policing and by being attacked for daring to challenge conventional stereotypes. Reshaping the online environment in ways that address this discrimination meaningfully requires a multifaceted approach that includes transparent, responsive, and accessible redress through both social media platforms and, where necessary, law enforcement agencies. Unfortunately, these institutions all too often fail to respond adequately when girls report acts of cyberviolence committed against them. This article illustrates this failure by drawing on lessons learned from coauthor Julie S. Lalonde’s experiences in advocating online for gender equality. It also raises the troubling concern of law enforcement deference to corporate terms of service rather than to Canadian law.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".