Metot | Not Hacker News!

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26 days ago

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It looks like metot.org uses a vote counting type approach to evaluate how many PRO and CON arguments were made and then uses the vote count to evaluate the strength of the argument. There are a lot of problems with this approach. Vote counting fails because: Epistemic naivety: Ignores evidence quality ; Structural blindness: Misses dialectical interactions; Semantic impoverishment: No context, no warrants, no hedging; Temporal insensitivity: Static snapshots of dynamic discourse; Fallacy tolerance: No rhetorical/logical error detection

Proper system requires: Deep NLP: Discourse parsing, semantic role labeling, entailment; Structured reasoning: AAF, probabilistic argumentation, Bayesian aggregation; Domain knowledge: Evidence hierarchies, causal inference, statistical meta-analysis; Explainability: Attention visualization, counterfactual reasoning, gradient-based saliency

hkcanan

26 days ago

Thanks for the feedback, but characterizing this system as “vote counting” is incorrect. Metot’s argument analysis uses a fundamentally different methodology. What We Actually Use:

1. Toulmin Model Analysis Each argument is analyzed for its full structure, not just PRO/CON:

• Claim: The specific assertion

• Evidence: Supporting facts, data, sources

• Warrant: The reasoning connecting evidence to claim

• Strength Score: 1-10 based on evidence quality, warrant clarity, and fallacy presence

2. Dialectical Mapping with Recursive Response Structure Contrary to “structural blindness,” our system tracks how arguments respond to each other recursively:

Argument 1 (Supporting) └── Response 1.1 (Opposing - objection) └── Response 1.1.1 (Supporting - rebuttal) └── Response 1.1.1.1 (Opposing - counter-rebuttal)

This captures unlimited depth of dialectical exchanges.

3. Logical Fallacy Detection Contrary to “fallacy tolerance,” we detect: circular reasoning, ad hominem, straw man, false dichotomy, hasty generalization, and others.

4. Context-Aware Type Assignment Argument type (supporting/opposing) is determined relative to the author’s thesis, not absolute. If the author criticizes Theory X, arguments against X are classified as “supporting.” This addresses semantic context.

5. Self-Validation Layer Before output, the system validates:

• Argument count (academic texts typically have 5-15+ distinct arguments)

• Depth check (most academic texts have 2-4 levels) • Balance check (detects one-sidedness)

• Type accuracy verification What We Acknowledge: • Single-pass analysis (no iterative refinement yet) • General academic analysis rather than domain-specific ontologies

I appreciate critical feedback, but the system is not vote counting. Feel free to test with a demo account.

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