Evidence Scores

Evidence and Argument Scoring: A Framework for the Idea Stock Exchange

Best Supporting Evidence

• List actual scientific studies or credible sources (e.g., Google Scholar, JSTOR).
• Provide real references if you can:
• [Author(s), Year, Title, Journal/Source, Link]
• Briefly explain why it’s relevant and credible.

What: Separate Scoring Systems for Evidence Quality and Linkage

1. Evidence Quality Scores

• Definition: Scores the inherent validity of evidence, independent of the context or its application.
• Evaluation Criteria:
• Internal Validity: Reliability of methodology, design, and execution.
• Statistical Metrics: Confidence intervals, effect sizes, and p-values.
• Replicability:
• Direct Replication: Exact reproduction of the original study.
• Systematic Replication: Testing with variations in methods or populations.
• Conceptual Replication: Testing the same hypothesis through different methodologies.

2. Evidence-to-Conclusion Linkage Scores

• Definition: Measures how well evidence supports or weakens a conclusion within a network of interconnected ideas.
• Evaluation Metrics:
• Network Strength: Assesses the performance of evidence in influencing the web of interconnected pro/con sub-arguments. The score depends on both the inherent strength of the evidence and how effectively it contributes to linked arguments.
• Relevance: Evaluates the direct applicability of the evidence to the specific conclusion being assessed.
• Contextual Fit: Considers the scope of the evidence, including its adaptability to broader or narrower applications.
• Uniqueness: Reflects the novelty of the evidence in providing additional information beyond existing data.

Why: The Need for Separate Scoring

1. Independence of Evidence Quality

• Evidence quality should measure intrinsic validity, separate from its application or interpretation.
• This separation prevents conflating strong evidence with weak conclusions or vice versa.

2. Dynamic Updates

• As new arguments or evidence arise, linkage scores adjust without altering the original quality score of the evidence.
• Supports evolving understanding and iterative refinement of conclusions over time.

3. Mapping Nuanced Relationships Across Applications

• Evidence often influences multiple conclusions with varying strengths. Independent scoring allows detailed mapping of its role across the network of arguments.

How: Implementation Strategy

1. Evidence Quality Assessment

• Use rigorous evaluation based on:
• Internal validity and robustness of methodology.
• Replication metrics to ensure reliability.
• Bias detection and independence checks.

2. Evidence-to-Conclusion Linkage Scores

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Linkage Score = (Network Strength × Relevance × Uniqueness)

• Network Strength: Measures the aggregated impact of evidence on interconnected pro/con arguments. Each node in the network adjusts dynamically based on the combined performance of its linked sub-arguments.
• Relevance: Captures the direct relationship between evidence and the conclusion it supports or weakens.
• Uniqueness: Evaluates the distinct contribution of the evidence, reducing redundancy from overlapping or repeated findings.

3. Adaptive Network Algorithms

• Adapt a PageRank-inspired system to evaluate how evidence and arguments influence conclusions within a network.
• Weights and Adjustments:
• Reflect the strength and relevance of arguments linked to conclusions.
• Include user input and algorithmic recalibration for ongoing updates.

4. Overall Conclusion Scoring Formula

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Conclusion Score = ∑[(A - D) × L × U]

Where:

• A: Pro argument scores, weighted by relevance and strength.
• D: Con argument scores, weighted similarly to A.
• L: Evidence-to-conclusion linkage scores.
• U: Uniqueness factors to minimize redundancy.

Example: Vaccine-Autism Hypothesis

Evidence Scoring:

• Evidence 1: A small, flawed study showing correlation (Low Evidence Quality Score).
• Evidence 2: Large-scale, well-conducted studies showing no correlation (High Evidence Quality Score).

Linkage Scoring:

• Linkage of Evidence 1: Weak (flawed methodology and correlation doesn’t equal causation).
• Linkage of Evidence 2: Strongly negative (high-quality evidence that contradicts the hypothesis).

Conclusion Impact:

• Evidence 1: Minimal positive impact due to weak linkage.
• Evidence 2: Strong negative impact due to high-quality evidence and strong relevance to refuting the hypothesis.

Continuous Improvement

1. Bias Auditing:

• Regularly review algorithms for systemic biases in scoring.
• Community Engagement:
• Encourage user feedback and voting to refine argument and evidence linkage.
• AI-Assisted Analysis:
• Use AI for clustering similar arguments, detecting redundancy, and monitoring biases.
• Transparent Processes:
• Provide clear explanations for scoring updates to foster trust and user understanding.