Bytes and Barriers: Addressing Technology-Facilitated Gender-Based Violence for Women and Girls with Disabilities - Roadmap
Bibliographic record
Abstract
The Bytes and Barriers Symposium was a one-day event, focused on technology-facilitated gender-based violence (TFGBV) against women and girls with disabilities, bringing together experts, practitioners, and stakeholders to discuss emerging trends, share research and insights, and foster collaboration amongst diverse attendees. The Symposium took place on Wednesday, May 28, 2025, hosted by the Canada Excellence Research Chair in Health Equity and Community Wellbeing (CERC HECW) Research Program at Toronto Metropolitan University (TMU). Bytes and Barriers aimed to critically explore the rising and urgent issue of TFGBV against women and girls with disabilities, highlighting its intersectional nature and distinct manifestations from mainstream experiences of TFGBV. Rooted in systemic and structural gender inequities and compounded by ableist stereotypes, TFGBV is a growing form of violence that often goes unrecognized or inadequately addressed in policy, advocacy, and service provision. During Bytes and Barriers, attendees and presenters unpacked the specific risks faced by women and girls with disabilities– ranging from digital illiteracy, economic precarity, social isolation, and caregiver dependence– to examine how these factors contribute to heightened vulnerability and reduce access to support and justice. In addition to exploring and raising awareness about these risk factors, the Symposium aimed to provide a forum to discuss: “What’s next?” As the facilitators of TFGBV begin to be unveiled, we must establish and begin translating this knowledge into actionable practice, in the roles and responsibilities which we take on as researchers, civil society members, leaders, practitioners, policy influencers, and advocates to support, uplift, and protect systemically vulnerable women– especially women with disabilities– to prevent TFGBV.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".