What makes or breaks competitive research proposals? A mixed-methods analysis of research grant evaluation reports

作者：

Highlights：

• We used machine learning and qualitative text analysis to study grant peer review.

• Cluster analysis revealed that content of review reports matched the predefined evaluation criteria.

• Outcome of grant peer review is more influenced by proposals' weaknesses than strengths.

• Features of review reports were consistent among disciplinary evaluation panels.

摘要

•We used machine learning and qualitative text analysis to study grant peer review.•Cluster analysis revealed that content of review reports matched the predefined evaluation criteria.•Outcome of grant peer review is more influenced by proposals' weaknesses than strengths.•Features of review reports were consistent among disciplinary evaluation panels.

论文关键词：European Commission,Machine learning,Marie Curie Actions,Peer review outcome,Qualitive analysis,Research grants,EC,European Commission,ERC,European Research Council,ESR,Evaluation Summary Report,FP7,Seventh Frameworks Programme,ITN,Initial Training Networks,MCA,Marie Curie Actions

论文评审过程：Received 4 June 2021, Revised 24 March 2022, Accepted 29 March 2022, Available online 13 April 2022, Version of Record 13 April 2022.

论文官网地址：https://doi.org/10.1016/j.joi.2022.101289