qualitative-pipeline: LLM quote extraction for interview research
A documented Python/Node tool targets qualitative interview analysis. Its inspected Python source asks an LLM for exact interviewee quotations relevant to a research question and saves structured extractions; the README describes optional visualization and a later verb-framing step. No code or model was run. A workflow level remains unspecified because substantive researcher output checking is not documented in the inspected sources.
Workflow level n/a Source: Repository-documented Outcome: Not empirically evaluated here
Research applicability
README plus static entry point and extraction implementation substantively establish cognitive-research interview quote extraction with question/example/model inputs and JSONL output. Useful research tool can be recommended without fabricating output review or a workflow level.
Getting started
Editorial untested suggestion: begin by reading the linked pinned primary documentation, define a small non-sensitive research task and researcher checks before considering separately authorised execution.
Maintenance
Dated source snapshot only; present availability, maintenance and version parity require separate checks.
License
Source visibility is not reuse clearance; source/model/data licenses and rights require separate checking.
Workflow & human oversight
Scope: Source-described intended research use only; no execution or benefit independently verified Human control: Question, examples and model are user configurable; visualization is described. Substantive researcher verification/correction of extracted quotes or categories is not established; visualization is not itself verification. Stage connections: README connects extraction to later framing by a user-run command. Static inspection establishes extraction code intent only, not completed end-to-end operation. Stopping: One extract-and-save call is shown; no empirical convergence, researcher validation stop or autonomous research loop documented. Missing evidence: Explicit researcher checking/correction of extraction and framing outputs is missing. Framing implementation and actual runtime/model behaviour remain unverified.
Workflow level is not an evidence score; higher is not better evidence.
Approach
- Source code configures LangExtract with research-question quote constraints and saves annotated output; actual adherence not tested
Outcomes (as reported by the originating team)
Documented method or intended capability; no empirical research benefit established by this review.
Limitations
- Static text inspection only; no candidate code, model, traces, services or tests executed. - Exact-quote instructions are prompt intent, not proof of faithful quotations or cognitive-category validity. - PromptValidationLevel.OFF is present; it does not establish absence of every other check. - Telemetry/export paths are configured in static code and README discusses provider keys; no transcript, key value or telemetry data accessed. - Local-model option in README is not a privacy guarantee; installation, data governance and trace handling remain unevaluated. - AI editorial review; no human editorial verification claimed.
Reproducibility
AI editorial source review only. No candidate code, model, agent, experiment or reported outcome independently executed or reproduced.
Adoption requirements
Editorial suggestions only, untested. Check source/version, rights, data handling, model and environment prerequisites before any separately authorised adoption; this review installs or invokes nothing.
Comments
No comments yet.
Add a comment
Comments are held for moderation before appearing. Critical or corrective comments are welcome — especially evidence that an entry overstates what has been verified.