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ARXIV:2604.19309 · QUALITATIVE ANALYSIS TOOLS · SUBMITTED 22 APR · 02:13 UTC · FRESHNESS STALE
ARXIV:2604.19309QUALITATIVE ANALYSIS TOOLSSUBMITTED 22 APR · 02:13 UTCFRESHNESS STALEAthikash Jeyaganthan · Kai Xu · Franziska Becker · Steffen Koch · arXiv
An AI-augmented platform for qualitative coding that provides real-time feedback on coding consistency to prevent interpretation drift, enhancing analysis credibility.
Opportunity summary
Pain An AI-augmented platform for qualitative coding that provides real-time feedback on coding consistency to prevent interpretation drift, enhancing analysis credibility.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
An AI-augmented platform for qualitative coding that provides real-time feedback on coding consistency to prevent interpretation drift, enhancing analysis credibility. As coding proceeds across large datasets, interpretations of codes often shift (temporal drift), reducing…
Qualitative coding relies on a researcher's application of codes to textual data. As coding proceeds across large datasets, interpretations of codes often shift (temporal drift), reducing the credibility of the analysis.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Existing Computer-Assisted Qualitative Data Analysis (CAQDAS) tools provide support for data management but offer no workflow for real-time detection of these drifts. Code availability…
Qualitative Analysis Tools moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
An AI-augmented platform for qualitative coding that provides real-time feedback on coding consistency to prevent interpretation drift, enhancing analysis credibility.
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Paper Pack
10.48550/arXiv.2604.19309An AI-augmented platform for qualitative coding that provides real-time feedback on coding consistency to prevent interpretation drift, enhancing analysis credibility.
Abstract
Qualitative coding relies on a researcher's application of codes to textual data. As coding proceeds across large datasets, interpretations of codes often shift (temporal drift), reducing the credibility of the analysis. Existing Computer-Assisted Qualitative Data Analysis (CAQDAS) tools provide support for data management but offer no workflow for real-time detection of these drifts. We present Co-Refine, an AI-augmented qualitative coding platform that delivers continuous, grounded feedback on coding consistency without disrupting the researcher's workflow. The system employs a three-stage audit pipeline: Stage 1 computes deterministic embedding-based metrics for mathematical consistency; Stage 2 grounds LLM verdicts within $\pm0.15$ of the deterministic scores; and Stage 3 produces code definitions from previous patterns to create a deepening feedback loop. Co-Refine demonstrates that deterministic scoring can effectively constrain LLM outputs to produce reliable, real-time audit signals for qualitative analysis.
Source availability
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Extraction status
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
An AI-augmented platform for qualitative coding that provides real-time feedback on coding consistency to prevent interpretation drift, enhancing analysis credibility. As coding proceeds across large datasets, interpretations of codes often shift (temporal drift), reducing the c...
METHOD
Qualitative coding relies on a researcher's application of codes to textual data. As coding proceeds across large datasets, interpretations of codes often shift (temporal drift), reducing the credibility of the analysis.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Existing Computer-Assisted Qualitative Data Analysis (CAQDAS) tools provide support for data management but offer no workflow for real-time detection of these drifts. Code availability is flagged in the p...
WHY NOW
Qualitative Analysis Tools moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 7, "author": "Athikash Jeyaganthan; Kai Xu; Franziska Becker; Steffen Koch", "title": "Co-Refine: AI-Powered Tool Supporting Qualitative Analysis"
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partial
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Concepts
Methods
Materials
Markets
Competitors
An AI-augmented platform for qualitative coding that provides real-time feedback on coding consistency to prevent interpretation drift, enhancing analysis credibility.
Segment
Qualitative Analysis Tools
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
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CITED BY
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 3 sources, 50% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
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Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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No CRM or outreach source attached.
People
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Gaps
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Regulatory need unclassified.
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People
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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