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Related Experiment Video

Updated: Jun 3, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Understanding belief using citation networks.

Steven A Greenberg¹

¹Department of Neurology, Division of Neuromuscular Disease, Brigham and Women's Hospital, Boston, MA, USA. sagreenberg@partners.org

Journal of Evaluation in Clinical Practice

|March 12, 2011

Summary

This summary is machine-generated.

Scholarly citations build credibility, but distortions can create unfounded scientific beliefs. This study explores methods to identify these citation distortions and study scientific belief systems.

Related Experiment Videos

Last Updated: Jun 3, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Bibliometrics
Scientific Communication
Information Science

Background:

Citation is crucial for establishing credibility in published scientific statements.
Non-scholarly uses of citation, termed 'citation distortions,' can lead to the widespread acceptance of unfounded beliefs.
Previous work introduced methodology for studying citation distortions and scientific belief systems.

Purpose of the Study:

To further discuss methodological approaches for analyzing published scientific belief systems.
To identify and understand the impact of citation distortions on scientific claims.

Main Methods:

Review and elaboration of methodological frameworks for studying scientific belief systems.
Analysis of citation practices to detect distortions.
Qualitative and quantitative assessment of citation networks and their influence.

Main Results:

Distinguishing between scholarly citation and citation distortion is key.
Methodological approaches can reveal patterns of belief formation and propagation.
Citation distortions can significantly impact the perceived validity of scientific claims.

Conclusions:

Further methodological development is needed to robustly study scientific belief systems.
Identifying citation distortions is essential for maintaining scientific integrity.
Understanding these dynamics can help mitigate the spread of misinformation in science.