From Stories to Structure

Narratives and questionnaires don't describe the same experience.

This demo lets you explore that mismatch in action.

Try the Demo ↓Main Repository ↗Article Page ↗

Autor original: Cristian Pulido

Why This Matters

NDEs can be represented in two ways that do not always match perfectly:

  • free narratives (rich, contextual, temporal), and
  • questionnaire-style labels (structured and standardized).

This demo helps you inspect that representational gap by showing the extraction process step by step.

Core Questions

  1. How much can narrative-derived signals align with questionnaire-style labels?
  2. Is narrative tone sufficient to recover self-reported valence?
  3. Are experiential-perceptual features easier to recover than reflective aftereffects?
  4. Does disagreement indicate model error, representational mismatch, or both?

What This Demo Reproduces

  • Section-level analysis of context, core experience, and aftereffects.
  • LLM extraction of tone, contextual framing, and structured features.
  • Evidence-focused outputs for transparent interpretation.
  • Optional valence comparison against overall experience-weighted tone.

Demo Purpose

This page demonstrates how an LLM can extract structured information from narrative text.

Use it to compare:

  • what the model extracts, and
  • what you would extract manually from the same narrative.

Model Scope in This Public Space

  • Default model is Qwen 3.5 0.8B local download (runs in this Space instance after first load).
  • Additional routed models are listed only when providers are live for conversational inference.
  • Routed usage depends on your Hugging Face account quota and provider availability.
  • Prompt templates are aligned with the main local workflow.
  • It is useful for fast public testing, but it is not equivalent to the full local research pipeline.
  • For the more solid and reproducible local workflow (with Ollama models and full experiment tooling), use: https://github.com/cristian-pulido/NDE_NARRATIVES_ANALYSIS

Try it yourself

  • Write or paste a narrative in the three section inputs.
  • Run the analysis.
  • Compare what the model extracts, what you would extract, and what a questionnaire would capture.

Quick flow

  1. Add your text.
  2. Run analysis.
  3. Compare interpretations.

Results Overview Video
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Important Disclaimer

  • This tool is for research and educational use only; it is not a medical or psychological diagnostic system.
  • Do not submit personally identifying or highly sensitive information.
  • Model outputs may be inaccurate, incomplete, or biased and require human review.

Input
✍️Input
🧩Extraction
🧠Interpretation
Model

Models stay routed/local exactly as configured for this HF app.
Optional Valence

Results

Study Insight (This Run)

Run an analysis to see how narrative and questionnaire-style interpretations align or diverge.

Interpretation