Faster insights: qualitative analysis of gun violence

Reproducibility matters: preserve raw transcripts, media citations, and metadata (date, speaker role, location).

Use consistent code labels for systemic factors (e.g., 'disinvestment', 'trauma', 'policy-cut', 'ARPA-unused').

Compare segments (age, race, neighbourhood) to quantify theme frequencies and sentiment around intervention trust.

Link themes to funding lines: identify where ARPA or DOJ funds could have been allocated to proven interventions.

Produce evidence packets: themed quotes + source metadata to support grant requests or oversight hearings.

Monitor policy changes over time by versioning collected documents and tracking theme shifts.

Translate themes into stakeholder artifacts: executive briefs, slide decks, and localized story maps.

Use co-occurrence visuals to show how 'trauma' clusters with 'youth employment' or 'school safety' in community narratives.

Design A/B tests for messaging informed by coded concerns (e.g., emphasizing jobs vs. policing).

Problem: News articles, interview audio, audit PDFs, and council transcripts are scattered.

Evidano: bulk ingest documents and spreadsheets; automatic OCR and searchable corpus creation makes every source queryable.

Problem: Coding drift across analysts.

Evidano: import or build a hierarchical codebook, run AI-assisted coding, and produce theme frequency tables and inter-coder consistency checks.

Problem: Hard to compare themes across age, neighborhood, or funding periods.

Evidano: run cross-segment contrasts (e.g., youth vs. adult, pre- vs. post-ARPA), and visualize trend lines for themes and sentiment.

Problem: Stakeholders want brief, sourced evidence.

Evidano: generate exportable reports with clickable quotes, annotated source links, word clouds, and co-occurrence networks you can embed in briefs.

Problem: Sensitive interviews and PII need protection.

Evidano: PII redaction, encryption, and an assurance that uploaded data isn't used to train third-party models, fit for ethics-conscious research.

Download NPR article, local council minutes, ARPA spending records, police reports, and community interviews.

Upload PDFs, audio, and spreadsheets to Evidano; run transcription (custom dictionary for local names) and OCR.

Create a codebook with nodes like 'funding use', 'trauma', 'youth-programs', run a 10% pilot, refine codes.

Apply AI-assisted thematic coding across the full corpus, then spot-check and adjust boundaries.

Run cross-segment frequency and co-occurrence analyses (e.g., quotes from parents vs. officials; pre- vs. post-policy).

Generate word clouds, co-occurrence networks, and hierarchical theme trees; compile an evidence packet with sourced quotes.

Export deliverables for funders/policymakers; collect feedback and version your corpus so future analyses are comparable.

A: Use balanced sampling, normalize by document counts, and run frequency-per-1k-words metrics. Evidano supports cross-segment normalization and bootstrapped confidence intervals.

A: Treat interview data as human-subjects material, get consent, redact PII, and store encrypted. Evidano includes PII redaction and secure storage options for research datasets.

A: Yes; AI in Evidano accelerates coding and surfacing candidate themes; final interpretive decisions remain with researchers.