Dynamic disruption index across citation and cited references windows: Recommendations for thresholds in research evaluation

2025-04-10

AI Summary

This study addresses the temporal instability of the Disruption Index (D) in scholarly evaluation, arising from citation time lags. We systematically investigate D-value evolution dynamics and optimal citation windows using a multi-disciplinary dataset of over ten million papers. Applying D-trajectory tracking, classification consistency analysis, and window sensitivity modeling, we identify discipline-specific convergence thresholds for D: a 10-year window achieves >80% final classification consistency; a 3-year window induces significant instability; and 60–80% of highly disruptive or consolidative works can be accurately identified within five years. Moreover, papers with high reference counts exhibit precocious D-value stabilization. Our findings provide an empirically grounded temporal calibration framework for quantitative research assessment, balancing early predictability with longitudinal validity—thereby establishing a robust, time-aware evaluation paradigm.

Technology Category

• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

The temporal dimension of citation accumulation poses fundamental challenges for quantitative research evaluations, particularly in assessing disruptive and consolidating research through the disruption index (D). While prior studies emphasize minimum citation windows (mostly 3-5 years) for reliable citation impact measurements, the time-sensitive nature of D - which quantifies a paper' s capacity to eclipse prior knowledge - remains underexplored. This study addresses two critical gaps: (1) determining the temporal thresholds required for publications to meet citation/reference prerequisites, and (2) identifying"optimal"citation windows that balance early predictability and longitudinal validity. By analyzing millions of publications across four fields with varying citation dynamics, we employ some metrics to track D stabilization patterns. Key findings reveal that a 10-year window achieves>80% agreement with final D classifications, while shorter windows (3 years) exhibit instability. Publications with>=30 references stabilize 1-3 years faster, and extreme cases (top/bottom 5% D values) become identifiable within 5 years - enabling early detection of 60-80% of highly disruptive and consolidating works. The findings offer significant implications for scholarly evaluation and science policy, emphasizing the need for careful consideration of citation window length in research assessment (based on D).

Problem

• Determining temporal thresholds for citation and reference prerequisites in research evaluation
• Identifying optimal citation windows balancing early predictability and longitudinal validity
• Analyzing D stabilization patterns across fields with varying citation dynamics

Innovation

• 10-year window for reliable disruption index
• Citations and references stabilize D faster
• Early detection of disruptive works possible