Bibliographic record
Abstract
Technology facilitated gender-based violence (TFGBV) is a complex worldwide phenomenon with devastating results. Research to date shows that victim-survivors of intimate partner violence are tracked by their abusive partners who use technology to monitor their movements and communication. Many women journalists, human rights defenders and politicians face daily death threats and rape threats for speaking out about equality issues or for simply being a woman in a leadership role. Those with intersecting marginalized identities are at specific risk, with Black, Indigenous, and people of colour, LGBTQ+ people, and people with disabilities facing higher rates of attacks and concerted attacks that specifically target their identities. These attacks create legitimate safety concerns, involve egregious invasions of privacy, and can have significant financial costs for those targeted, however, one of the most serious impacts is the silencing of women’s and transgender people’s voices in digital spaces. TFGBV makes it unsafe and unwelcoming for women and transgender people to express themselves freely in a world where digital communication has become one the primary modes of communication, particularly during the COVID-19 pandemic.To better understand TFGBV, CIGI and the International Development Research Centre (IDRC) have embarked on a two-year research project titled “Supporting a Safer Internet: Global Survey of Gender-based Violence Online” in order to examine women’s and LGBTQ+ people’s experiences with technology-facilitated violence globally. In 2021, this project will survey representative samples of people in 18 countries, the majority of which are lower- and middle-income countries, to learn about people’s experiences with TFGBV in these regions. The goal of this research is to specifically learn more about the experiences of people in the Global South, where there is a dearth of empirical data on TFGBV.As the first publication in this series, this paper serves as an introduction to TFGBV and many of the concepts that will serve as the basis for this research project. Relying on existing research to date on TFGBV, this paper reviews some of the more common forms of TFGBV, including harassment, image-based sexual abuse, the publication of personal information (doxing), stalking, impersonation, threats and hate speech. Following that it notes who is at greatest risk of being targeted by TFGBV, including victim-survivors of intimate partner violence, women in leadership positions, and women and LGBTQ+ people with intersecting marginalized equality factors. Finally, it highlights research that has identified the individual and systemic harms targets of TFGBV , including psychological and emotional harms, privacy, safety, speech restrictions, and economic harms.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".