abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Determine what observation would falsify a hypothesis and flag unfalsifiable formulations.
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Determine what observation would falsify a hypothesis and flag unfalsifiable formulations.
name: evaluate-falsifiability description: "Determine what observation would falsify a hypothesis and flag unfalsifiable formulations."
Evaluate whether a claim or hypothesis exposes observations that could count against it.
required: [claim, prediction_set, observation_domain] optional: [auxiliary_assumptions, measurement_limits] constraints: [falsifying conditions must be observable within the declared domain]
1. Translate the claim into testable predictions and boundary conditions. 2. Identify observations that would contradict the claim under its assumptions. 3. Check whether those observations are measurable and independent of the claim's definition. 4. Classify falsifiability and list needed operationalization.
produces: [falsifiability_assessment, falsifying_observations, operationalization_gaps, assumption_dependencies] delta_fields: [findings, hypothesis_updates, uncertainties, open_questions]
Do not call a claim falsifiable merely because it can be criticized rhetorically.
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
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Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.
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