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April 5, 2026Open Access

Freshness Decay in Academic References: Measuring Citation Shelf Life Across AI Research Domains

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Key Points

This research aims to quantify how quickly academic references lose relevance over time in AI domains.
Analyzed citation patterns across various AI research publications
Utilized bibliometric techniques to measure reference decay
Categorized findings by different AI subfields
Found significant variance in citation decay rates across AI domains
Older references show a marked decrease in citation frequency
Certain AI subfields maintain higher citation relevance over time

Implication

Research article measures citation shelf life in AI domains, suggesting implications for academic validity.

Abstract

Research article: Freshness Decay in Academic References: Measuring Citation Shelf Life Across AI Research Domains

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Cite This Study

Oleh Ivchenko (2026) studied this question.

synapsesocial.com/papers/69d49fa9b33cc4c35a228289 https://doi.org/10.5281/zenodo.19428758

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