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lab-notes.aiPublic-service AI playbooks
Opportunity 02/17CommunitiesHousing and communities

Community Participation Analysis

Explore how public comments on local issues might be grouped into themes while keeping the limits of participation counts visible.

Published sources
2
Starter data
18 synthetic records
Domain build partner
build-community-participation
Last reviewed

From the draft strategy

Opportunity

The draft strategy names AI for community-led social change as a potential public-service application, encouraging people to use data to change things where they live, without saying who is taking part, who is missing, or who ends up deciding.

Where the draft says it

Table 2 — potential public-service applications

Read the draft strategy source (opens in a new tab)

Source register

Research already done

Real published source

We reviewed these published sources so you do not have to start from zero. Each source shows what it covers, how it can be accessed, and why it matters.

2 sources

  1. nidirect

    Open

    Northern Ireland consultations portal

    What it covers
    The consultation platform used by several Northern Ireland departments and public bodies, listing what is open, forthcoming, and closed, with a separate portal carrying the remaining departments.
    Why it matters here
    It is where public comment on local and policy questions is actually gathered here, and the split across two portals is part of why participation is uneven in the first place.

    Open the source (opens in a new tab)

  2. Department for the Economy

    Open

    Draft Circular Economy Strategy — public consultation response report

    What it covers
    How one Northern Ireland department grouped, counted, and reported back on the responses to a public consultation.
    Why it matters here
    It shows what happens to comments after they are submitted, which is the step this example would be automating, and its vocabulary shaped how the synthetic comments are grouped.

    Open the source (opens in a new tab)

Starter data

Starter dataset

Synthetic working data

Use 18 synthetic comments across five local topics to explore theme grouping without holding anyone's submitted words.

AI-assisted research helped identify and interpret the published sources. We then created a small, non-sensitive synthetic dataset shaped by the information those sources expose. It is for exploration—not evidence, training, or operational decisions.

Records
18
Fields
4

Field preview

records view
  • id

    SYN-CP-01 · SYN-CP-02 · SYN-CP-03

  • topic

    bus-service-changes · park-and-green-space · planning-and-housing

  • stance

    critical · supportive · uncertain

  • text

    The new timetable went up as a leaflet in the library and nowhere else. The first I knew about losing the evening bus was standing at the stop at half nine wondering where it was. If this was open for eight weeks, it did not reach our end of the road. · The extra morning service has made a real difference getting the children to school. I would only ask that it keeps running through the summer, because childcare does not stop in July. · Hard to say yet. On paper more buses is better. The ones added are at times that suit the depot rather than times anyone here is actually travelling, so I do not know whether it helps or not.

How it was prepared

AI authored fictional comments and stances to preserve disagreement and minority views found in public submissions.

Limitations

  • Eighteen comments are far fewer, tidier, and more evenly spread across topics than any real set of responses.
  • The five topics and four stances are this project's own choices, checked against no official framework.
  • A synthetic set cannot contain the thing that matters most about participation: who did not write in, and why.

Clone and build

Domain build partner

Build with a domain-aware coding agent

Clone the repository, then ask your coding agent to load the skill that ships with it. The skill brings the opportunity, sources, starter data, known unknowns and constraints into the conversation before anything is proposed. It follows the open Agent Skills standard, so it works in Claude Code, Codex and any other agent that reads skills.

01 / Clone

git clone https://github.com/Hypership-Software/lab-notes.ai.git

02 / Invoke

Claude Code
/build-community-participation
Codex
$build-community-participation

Conditions, not a checklist

Before you build

These are not footnotes. They are conditions that any responsible prototype would need to address.

  • Participation counts are not public opinion

    A count of comments measures who had the time, the connection, and the confidence to write, not what a community thinks.

  • Grouping can hide a sharp objection

    Automatic grouping tends to lose the single sharp objection, and the single sharp objection is often the one worth reading.

  • Categories must remain contestable

    People whose words are being grouped should be able to see the categories and argue with them, which is a design commitment rather than a technical one.