Data Validation for Gender-Based Violence Sentencing Cases

We’re seeking a detail-oriented volunteer to support data validation and consistency checks for sentencing decisions in gender-based violence (GBV) cases. Our legal team has already extracted key information from court decisions into structured formats, and now we need help reviewing the data to ensure it meets formatting standards, contains no typos, and reflects consistent logic across related fields.

This role does not require legal experience, but would benefit from someone with a background in data cleaning, quality assurance, or legal research. The volunteer will receive training in our methodology and will learn how to spot subtle inconsistencies—such as logical mismatches between related variables or sentencing calculations that don’t add up. If a case passes the initial checklist, it won’t need further review; if not, the case is opened, re-checked, and flagged for revision. Reviewing a flagged case typically takes 10–15 minutes.

⚠️ Trigger warning: These cases involve GBV offenses, and some may contain sensitive or disturbing content.

My goal for this Project is to create a validated dataset of GBV sentencing decisions that can be confidently used in high-impact research and advocacy tools. The volunteer will help refine our legal data outputs for accuracy and reliability, with a focus on a batch of ~50–100 cases during the engagement. This work is essential to scaling our automation systems and improving the quality of insights in our TrackGBV project.

Through working with our organization on this project, the volunteer will support ICAAD’s mission to dismantle systemic discrimination and strengthen access to justice for women and girls. The TrackGBV initiative is a groundbreaking effort to expose patterns of gender bias in legal systems using data from over 3,000 GBV cases. Your contributions will directly impact the quality and credibility of a public-facing dashboard used by human rights advocates, researchers, and judges across the Pacific to push for more equitable judicial outcomes.

We’ve developed a comprehensive handbook that defines each data point and outlines how to apply validation logic. Our team has also prepared a structured QC workflow, including examples and a checklist. You’ll be trained by our Data Analyst on how to spot inconsistencies—such as where one field implies another must be true, or where sentence calculations are logically misaligned. You’ll receive ongoing support and feedback, and all materials will be shared in a secure, organized format.