Analysis
Manual theme workspace and candidate review
Construct, organize, relate, merge, archive, and version human themes while keeping automatic themes as reviewable suggestions.
Implemented task
Public task
Open Themes and create or inspect a human-owned theme.
Expected outcome: Theme structure, relations, evidence, counterexamples, and history remain explicitly human-reviewable.
Declared product contract
Inputs and task boundary
Inputs
- Reviewed project codes and evidence
- Optional automatic theme candidates
Input constraints
- Automatic candidates remain separate suggestions and never overwrite human structure.
User actions
- Create or review a theme.
- Add member codes, evidence, counterexamples, hierarchy, and directed relations.
- Merge, archive, restore, or inspect theme history.
Outputs
- A versioned human-owned theme structure with traceable evidence and relations.
Decision boundary
Where it fits
- Researchers constructing and challenging themes without surrendering ownership to automation.
Outside the boundary
Where it does not fit
- An automatically generated final theme structure.
Verification
Product proof
journey
proof.x-r02-theme-workspace
- Verified
- Review due
- Expires
- Researchers create, relate, merge, archive, and version human-owned themes.
- Automatic reruns do not overwrite human structures and withdrawn evidence is minimized.
- Large theme structures remain virtualized with an equivalent keyboard path.
Failure boundary: Complete thematic reporting remains an M6 deliverable.
OpenVerbatim is an open-source (Apache-2.0) qualitative data analysis platform for coding and analyzing interview transcripts. AI-suggested codes stay marked as suggestions until a human reviewer confirms or rejects them, and every decision is kept in an audit trail. The full feature set is available when self-hosted; there is no paid feature wall.