When governments publish consultation summaries, researchers and policy teams must ask: does the report reflect submissions, or a chosen framing? The WA Burswood Park consultation (reported 22 Aug 2025) highlights that risk, the government said 76% of contributions provided “positive ideas”, while local groups collected evidence they say shows strong opposition. This post shows how AI-enabled qualitative analysis of community consultation can (1) surface framing and mismatches between channels, (2) quantify sentiment and theme prevalence reliably, and (3) produce reproducible, shareable outputs for stakeholders. Read the original ABC report and then follow a short workflow you can run in www.evidano.com to audit, validate and visualise public feedback.
Fast take: what the source says
A recent ABC News piece (Cason Ho, published 22 Aug 2025) describes a dispute over the WA government’s summary of public feedback on a proposed $217 million Burswood Park entertainment precinct and racetrack.
- Government report claims “76% of all contributions provided positive ideas” (Perth Entertainment and Sporting Precinct report).
- Save Burswood Park Alliance captured screenshots and says 85% of online responses opposed the racetrack; 98% of drop-in post-it notes opposed it.
- There were 672 contributions via the government’s online consultation platform.
- Source: www.abc.net.au/news/2025-08-23/wa-community-groups-slams-feedback-report-for-burswood-racetrack/105408378
Findings snapshot
What happened (plain English)
The WA government ran a public consultation on a multi-use precinct at Burswood Park that includes a 20, 000-seat music bowl and a motorsport circuit. After the first consultation phase it issued a report highlighting positive ideas and industry support, summarised as 76% positive contributions.
- Community groups argue the consultation instrument did not offer a simple 'reject' option, so people who opposed the racetrack added conditional or mitigations (e.g., 'more trees', 'protect wildlife'), which the government tallied as 'positive ideas'.
- The groups documented screenshots and photos indicating far higher opposition rates in both online submissions (85%) and drop-in notes (98%).
- Key tension: framing effects in question design and the coding rules used to classify free-text submissions.
So what for researchers, UX teams and policy analysts
For qualitative researchers
Watch for ecological fallacies: aggregate 'positive ideas' can mask opposition to specific elements (here, motorsport).
Document coding rules and publish a codebook so stakeholders can reproduce counts.
For UX / consultation designers
Form design matters: include explicit opt-out/reject options and capture conditional responses separately.
Triangulate channels: online forms, drop-in notes, and emailed submissions should be analysed together, then compared by segment.
For policy & communications teams
Be transparent about how qualitative inputs were classified before issuing headline statistics.
Expect channel-based pushback; publish raw excerpts or anonymised quotes to reduce disputes.
Do this with AI-enabled qualitative analysis (how Evidano helps)
Ingest and preserve raw inputs
Import PDFs, screenshots, emailed submissions, and post-it photos into Evidano so all signals are in one corpus.
Evidano preserves originals and timestamps, critical for audit trails when stakeholders challenge summaries.
Detect framing & coding bias
Run thematic extraction and ask the platform to surface how 'positive' labels were applied across text fragments.
Use Evidano's codebook import and AI-assisted coding to reclassify 'positive ideas' vs explicit opposition, then compare counts by code.
Compare channels & segments quantitatively
Generate cross-segment analyses (online vs drop-in vs emailed) showing theme prevalence, quote exemplars, and co-occurrence networks to reveal whether opposition concentrates in certain channels.
Export visual reports for briefings or the EPA submission.
Produce reproducible, shareable outputs
Evidano creates clickable reports with quotes linked to sources so independent reviewers can verify classifications.
Data is encrypted and never used to train third-party models, addressing common privacy concerns.
7-step checklist: Audit a contested consultation in two weeks
Step 1; Collect
Gather the government report, raw export of the consultation platform, photos of drop-in notes and any emailed submissions.
Step 2; Ingest
Upload all files to Evidano; tag by channel and date.
Step 3; Auto-extract themes
Run thematic extraction and frequency analysis to surface dominant topics and sentiments.
Step 4; Import codebook
Load or create a codebook that separates 'positive suggestions' from explicit opposition and conditional comments.
Step 5; Recode & compare
Use AI-assisted coding to recode the corpus, then run cross-segment comparisons (channel × theme).
Step 6; Validate
Spot-check coded excerpts, adjust rules, and lock the codebook to freeze counts for reporting.
Step 7; Report
Export a short interactive report with counts, charts, and linked quotes to share with stakeholders or append to submissions.
Conclusion, audit faster, defend conclusions
The Burswood Park story is a practical lesson: headline percentages can hide how questions were asked, how text was coded, and how different channels reflect different views. Teams that need to defend consultation outcomes should make raw inputs, coding rules and cross-channel comparisons standard practice.
- Want to reproduce the counts and produce a shareable audit trail? Start a pilot in www.evidano.com and run the 7-step checklist on the Burswood corpus or your next consultation.
- For reproducible thematic, frequency and cross-segment analysis with secure data handling, visit www.evidano.com to request a demo or pilot.
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