Scholar Reasoning Distiller: Eight-Layer Extraction Framework

What It Solves

Asking a model to “imitate a scholar” often stops at tone and vocabulary. The output may sound familiar, but the scholar’s ordering of judgments, evidence preferences, and refusal boundaries can still drift. This skill treats the problem in two stages: first extract a reusable reasoning fingerprint from scholar-authored texts, then use that fingerprint as a hard constraint for peer review, supervision, lecturing, and panel discussion.

How It Works

• Distillation: It extracts an eight-layer framework covering L1 ontological framing, L2 conceptual distinctions, L3 analytical operations, L4 evaluative thresholds, L5 citation networks, L6 rhetorical rhythm, L7 refusal boundaries, and L8 diachronic change.
• Confidence tagging: Features are marked as stable, speculative, or questionable. Source type affects weight, and the fingerprint receives a maturity label such as v0.1, v0.5, or v1.0.
• Deployment: The system then runs through nine checkpoints: Scope, Activation, Ontological, Procedural, Evaluative, Intertextual, Rhetorical, Refusal, and Provenance. Responses are not free-form; they first restate the object of analysis and then follow the scholar’s operational order.
• Task output: For paper review, doctoral supervision, teaching, and panel debate, it produces structured results with quantitative scores and qualitative comments, including the closest match to the scholar and the most likely deviation.

Boundaries

This skill fits personal study, critical writing, teaching simulation, and academic discussion prep. The source material explicitly discourages commercial deployment, fake peer review, and academic misconduct. Lower fingerprint maturity should reduce confidence, and model capability differences will affect the fidelity of the reconstructed reasoning style.