Confidence and provenance
Every score exposes freshness, survey coverage, field reporting, community coverage, source diversity and model confidence.
- Seven weighted contributors
- Coverage distinguishable from absence
- Model version on every estimate
Use Cases · Research & Academia
Real-world data, methodology intact.
Administrative data is usually either unavailable or arrives stripped of the context that makes it usable — no sample design, no coverage information, no sense of what is missing. Civiq.plus keeps provenance and confidence attached to the record, and exposes them on export.
Provenance
On every score
7 factors
In the confidence model
Snapshots
Point-in-time comparison
Aggregate
Default analysis unit
The problem
What teams tell us before they start
These are the patterns that come up most often in scoping conversations for this kind of organisation.
Data arrives without its caveats
An extract gives you counts but not coverage, so it is impossible to distinguish a place with no problems from a place with no reporting.
No point-in-time reconstruction
Systems that only hold current state cannot answer what was known on a given date, which rules out most before-and-after designs.
Individual-level data is a liability
Research access to person-level civic data creates privacy exposure that is rarely proportionate to the analytical gain.
Selection effects are invisible
Feedback arrives from the people who have the means and confidence to report, and nothing in the data marks that skew.
Capabilities
What Civiq.plus brings to it
Historical snapshots
Periodic snapshots let you reconstruct what the state of a geography was on a given date, not just what it is now.
- Point-in-time reconstruction
- Pre and post windows
- Append-only event history
Aggregate by design
The platform is built for aggregate geographic, cohort and community intelligence — not covert individual-level profiling.
- No hidden individual persuasion profiles
- PII minimisation and separate identity vault
- Aggregate cohort analysis
Survey metadata preserved
Sample design, response mode, field dates, weighting method and representativeness labels travel with the result.
- Mode recorded per response
- Weighting method stated
- Non-representative polls labelled
Permission-scoped export
CSV, JSON and geospatial export scoped to what the requesting account is entitled to, with no uncontrolled personal data.
- Export scoped to permissions
- Geospatial formats supported
- Export events audited
Study design support
Impact studies record their comparison method and known confounders as structured fields rather than prose.
- Control or comparison method
- Confounder registry
- Confidence interval and grade
Outcomes
What changes in practice
- Extracts that carry their own coverage and confidence
- Point-in-time state for before-and-after designs
- Aggregate units that reduce privacy exposure
- Survey results with sample design attached
- Selection effects that are visible rather than assumed away
“The useful part is not the count. It is knowing how much of the district the count actually covers.”
A note from a policy research collaboration
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