Analysis
Manual multimodal coding
Select text, page or image regions, table cells, or media ranges and apply one or more human codes.
Implemented task
Public task
Open a review item or select uncoded evidence.
Expected outcome: A human-authored or human-reviewed evidence decision is persisted with exact revision history.
Declared product contract
Inputs and task boundary
Inputs
- A selected text range, page or image region, table range, or audiovisual interval
- One or more project codes
Input constraints
- The evidence locator must belong to the selected project source and readable revision.
User actions
- Select exact evidence in its source reader.
- Search or choose one or more codes and add a reason where needed.
- Save the human-authored coding.
Outputs
- Independent human coding records with typed evidence locations and audit history.
Decision boundary
Where it fits
- Manual coding across supported text, document, image, table, audio, and video evidence.
Outside the boundary
Where it does not fit
- Evidence outside the current project or an unreadable historical revision.
Verification
Product proof
journey
proof.m2-analysis-kernel
- Verified
- Review due
- Expires
- All five locator kinds, overlap, multiple codes, and exact evidence return are covered.
- Codebook, memo, history, research connections, and withdrawal child journeys pass.
- Every child artifact is hashed and rerun from a fresh database.
Failure boundary: The proof covers the completed M2 kernel, not M3 cases, matrices, or later collaboration rounds.
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.