Can peer reviewed scores be predicted by machine learning?
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Machine learning has shown promise in predicting peer review scores for academic articles. Research has explored using machine learning algorithms to estimate article quality based on bibliometric inputs and metadata, achieving accuracy above baseline levels in certain disciplines. Additionally, a proposed platform aims to enhance peer review accuracy and timeliness by utilizing peer prediction algorithms, addressing inefficiencies in early-stage research. Furthermore, machine learning has been investigated to predict journal citation thirds as proxies for article quality scores across various narrow fields, demonstrating above-baseline prediction accuracy with specific classifiers. These findings suggest that machine learning can play a valuable role in predicting peer review scores, potentially streamlining the evaluation process and enhancing the objectivity and efficiency of academic assessments.
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| Papers (5) | Insight |
|---|---|
| Machine learning can predict peer-reviewed scores of academic journal articles using citations and metadata, achieving above baseline accuracy in all 326 narrow fields. | |
29 Mar 2023 | Peer review scores can be predicted by machine learning algorithms, where human raters compete against a benchmark algorithm, enhancing accuracy and timeliness in peer review processes. |
1 Citations | Peer review scores can be predicted by machine learning using a variation of Peer Truth Serum for Crowdsourcing, as described in the paper on designing a marketplace for peer review. |
11 Dec 2022 | Yes, peer review scores can be predicted using machine learning algorithms like Random Forest and Extreme Gradient Boosting, achieving up to 42% accuracy above baseline in certain disciplines. |
| Yes, machine learning can predict peer review scores for articles submitted to the U.K. Research Excellence Framework 2021, achieving up to 42% accuracy above baseline in certain disciplines. |
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