探索机器学习对研究影响的专家评估的应用
Kate Williams1, Sandra Michalska2, Eliel Cohen2
1School of Social and Political Sciences, University of Melbourne, Melbourne, Victoria, Australia.
机器学习模型可以预测学术研究的影响,模仿人类专家的评估. 然而,由于机构背景和研究吸收等影响因素,建议对自动评估保持谨慎.
科学领域:
- 图书统计学 图书统计学
- 人工智能的人工智能
- 研究评估研究评估
背景情况:
- 评估学术研究的影响对于资金和政策至关重要.
- 传统的人力专家评估是资源密集型和主观的.
- 大规模的数据分析为更客观,更有效的评估提供了潜力.
研究的目的:
- 调查用于评估学术研究影响的机器学习 (ML) 应用程序.
- 将ML模型的预测与人类专家的判断进行比较.
- 确定影响基于ML的影响评估的关键特征.
主要方法:
- 利用公开可用的英国研究卓越框架 (REF) 2014年影响案例研究数据.
- 通过使用定性和定量特征 (机构,学科,叙事风格,文献计量,政策指标) 训练了五个ML模型.
- 评估模型性能,使用准确度预测高分和低分的案例研究.
主要成果:
- 在预测研究影响方面,ML模型的准确性与人类专家评估相美.
- 机构背景,叙事风格指标和政策/学术受众的研究吸收显著影响了模型预测.
- 影响案例研究的鉴定特征受ML方法的青.
结论:
- 机器学习显示出将研究影响评估从描述性转变为预测性的前景.
- 机器学习可以处理复杂的数据,以告知评估决策.
- 由于潜在的偏见和影响因素,建议在部署ML进行研究影响的自动评估时谨慎行事.
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