Automatic Detection of Research Values from Scientific Abstracts Across Computer Science Subfields
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909511638319104 |
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| author | Jiang, Hang August, Tal Soldaini, Luca Lo, Kyle Antoniak, Maria |
| author_facet | Jiang, Hang August, Tal Soldaini, Luca Lo, Kyle Antoniak, Maria |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Detection of Research Values from Scientific Abstracts Across Computer Science Subfields Jiang, Hang August, Tal Soldaini, Luca Lo, Kyle Antoniak, Maria Computation and Language Digital Libraries 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. |
| title | Automatic Detection of Research Values from Scientific Abstracts Across Computer Science Subfields |
| topic | Computation and Language Digital Libraries |
| url | https://arxiv.org/abs/2502.16390 |