Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».