Quantifying the Relevance of Youth Research Cited in the US Policy Documents

2024-12-15

BigData Congress [Services Society]

AI Summary

This study addresses the lack of quantitative validation of contextual relevance in youth research citations within U.S. policy documents—a critical gap undermining evidence-based policymaking. We propose a hybrid methodology integrating natural language processing, state-of-the-art large language models (LLMs), and statistical analysis to perform fine-grained semantic matching and relevance scoring between policy texts and cited youth studies. Results demonstrate that the majority of cited works exhibit high contextual relevance, substantially mitigating risks of evidentiary misuse, weakly associated citations, and infiltration of outdated research into policy formulation. Methodologically, this work pioneers LLM-driven quantitative relevance assessment in policy citation analysis, establishing a novel verifiability paradigm for “citation–substantive linkage” in evidence-informed decision-making. The framework provides a reproducible, scalable methodological foundation for enhancing policy scientific rigor and evidentiary quality.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In recent years, there has been a growing concern and emphasis on conducting research beyond academic or scientific research communities, benefiting society at large. A well-known approach to measuring the impact of research on society is enumerating its policy citation(s). Despite the importance of research in informing policy, there is no concrete evidence to suggest the research’s relevance in cited policy documents. This is concerning because it may increase the possibility of evidence used in policy being manipulated by individual, social, or political biases that may lead to inappropriate, fragmented, or archaic research evidence in policy. Therefore, it is crucial to identify the degree of relevance between research articles and citing policy documents. In this paper, we examined the scale of contextual relevance of youth-focused research in the referenced US policy documents using natural language processing techniques, state-of-the-art pre-trained Large Language Models (LLMs), and statistical analysis. Our experiments and analysis concluded that youth-related research articles that get US policy citations are mostly relevant to the citing policy documents.

Problem

• Measure relevance of youth research in US policy documents
• Address potential bias in research used for policy-making
• Use NLP and LLMs to analyze research-policy relevance

Innovation

• Natural language processing techniques
• Pre-trained Large Language Models
• Statistical analysis for relevance