Digital Gender Violence in Sri Lanka: A review of emerging trends, legal frameworks and policy gaps
Bibliographic record
Abstract
Digital Gender Violence (DGV) is a rising concern in Sri Lanka, manifesting through cyberstalking, image-based abuse, doxing, and impersonation. These harms disproportionately affect women, LGBTQ+ persons, and other marginalised groups. Despite growing public awareness, legal and policy responses remain fragmented and underdeveloped. This review addresses a critical gap by synthesising emerging trends, legal frameworks, and international best practices through an interdisciplinary, gender-sensitive lens. The study aims to answer four key questions: the nature of DGV manifestations, the effectiveness of existing legal and regulatory frameworks, the gaps in policy, enforcement, and digital literacy, and how international models can inform Sri Lankan reform. A narrative review method was employed, integrating peer-reviewed literature, legal documents, and institutional reports from 2015 to 2025. Thematic analysis was guided by feminist theory and digital governance perspectives. Findings reveal that legal instruments, such as the Online Safety Act (2024), lack precise definitions and clarity in enforcement. Policy coordination is weak, digital literacy remains low, and platform accountability is minimal. Vulnerable populations face compounded risks due to intersectional barriers related to gender, class, disability, and sexuality. Comparisons with Australia, Canada, and the Philippines highlight legal innovations that could be adapted to the Sri Lankan context. This review contributes a structured, critical analysis of DGV in Sri Lanka, advocating for survivor-centred legal reform, inclusive education, and mandatory platform regulation. The study underscores the urgency of a comprehensive, rights-based national policy on digital gender violence and suggests future research should prioritise underrepresented groups and longitudinal data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".