Civis
Connecting seven years of consultation data to trace how citizen feedback shaped India’s data protection framework
Civic Tech, Public Policy, Active Citizenship, Governance
Policy Research, Participatory Governance
Comparing feedback across multiple rounds used to mean holding several spreadsheets in your head at once and manually cross-referencing them. Dots collapsed that into a single structured view
About the partner
Civis is a community-driven, civic-tech non-profit founded in 2018 working to strengthen citizen participation in India’s lawmaking process. The organisation works to increase public awareness, provide education on issues of civic importance, and build effective dialogue between governments and citizens on draft laws and policies at the national, state, city, and ward level
Outcome of the study
With Dots, Civis could bring together six rounds of consultation data across seven years into a single structured view, allowing the team to trace how feedback on each provision evolved, and study shifts in opinion and public sentiment alongside policy evolution
Needs for Civis Team
1
Bring together six rounds of consultation, spanning seven years, with hundreds of open-ended responses and long policy documents into one comparable structure, without losing context
2
Tag feedback to individual policy clauses, rather than just consultation rounds, making it possible to trace the history of each provision
3
Trace and measure changes in opinion and shifts in public sentiment across multiple rounds of consultation and across consultations
Solution Snapshot
1
Policy documents and consultation responses imported as linked datasets, with each response tagged to its policy and consultation round
2
A flexible, multi-tiered thematic framework built around policy clauses, allowing the team to tag feedback by individual provision as well as broader themes.
3
A shared consultation identifier linking each response back to its source policy document, keeping feedback anchored to the text that prompted it.
4
AI-assisted search and summaries of tagged data, enabling quick search and synthesis of feedback on specific clauses across all six rounds without manually reviewing every response
Structured Data Import
Turn raw CSV data containing long-form policy feedback into clean, searchable data.
Make large volumes of consultation feedback easier to search, analyze, and compare across policy provisions and consultation rounds.
Clause-Level Themes Manager
Tag feedback across the six rounds of consultation by policy clause
Enables the history of each provision to be traced over time, making it easier to compare feedback across rounds and see how perspectives evolved
AI-Assisted Search & Summaries
Search and summarize feedback on a given clause or theme across the complete dataset, with links back to the original responses
Accelerates early exploration of the full dataset by quickly surfacing relevant feedback, broad sentiment, and directional summaries.
Using the Themes Manager, tagging feedback to the specific provision it responds to allowed us to trace how views on one issue evolved over time instead of only comparing round against round.