Automatic Detection of Research Values from Scientific Abstracts Across Computer Science Subfields

• URL: http://arxiv.org/abs/2502.16390v2
• Date: Wed, 26 Feb 2025 00:43:11 GMT
• Title: Automatic Detection of Research Values from Scientific Abstracts Across Computer Science Subfields
• Authors: Hang Jiang, Tal August, Luca Soldaini, Kyle Lo, Maria Antoniak,
• Abstract summary: It is crucial to explore what specific research values, known as basic and fundamental beliefs that guide or motivate research attitudes or actions.<n>Prior research has manually analyzed research values from a small sample of machine learning papers.<n>This paper introduces a detailed annotation scheme featuring ten research values that guide CS-related research.
• Score: 32.82061305764996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of Computer science (CS) has rapidly evolved over the past few decades, providing computational tools and methodologies to various fields and forming new interdisciplinary communities. This growth in CS has significantly impacted institutional practices and relevant research communities. Therefore, it is crucial to explore what specific research values, known as basic and fundamental beliefs that guide or motivate research attitudes or actions, CS-related research communities promote. Prior research has manually analyzed research values from a small sample of machine learning papers. No prior work has studied the automatic detection of research values in CS from large-scale scientific texts across different research subfields. This paper introduces a detailed annotation scheme featuring ten research values that guide CS-related research. Based on the scheme, we build value classifiers to scale up the analysis and present a systematic study over 226,600 paper abstracts from 32 CS-related subfields and 86 popular publishing venues over ten years.

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