  This literature review identifies indicators that associate with higher
impact or higher quality research from article text (e.g., titles, abstracts,
lengths, cited references and readability) or metadata (e.g., the number of
authors, international or domestic collaborations, journal impact factors and
authors' h-index). This includes studies that used machine learning techniques
to predict citation counts or quality scores for journal articles or conference
papers. The literature review also includes evidence about the strength of
association between bibliometric indicators and quality score rankings from
previous UK Research Assessment Exercises (RAEs) and REFs in different subjects
and years and similar evidence from other countries (e.g., Australia and
Italy). In support of this, the document also surveys studies that used public
datasets of citations, social media indictors or open review texts (e.g.,
Dimensions, OpenCitations, Altmetric.com and Publons) to help predict the
scholarly impact of articles. The results of this part of the literature review
were used to inform the experiments using machine learning to predict REF
journal article quality scores, as reported in the AI experiments report for
this project. The literature review also covers technology to automate
editorial processes, to provide quality control for papers and reviewers'
suggestions, to match reviewers with articles, and to automatically categorise
journal articles into fields. Bias and transparency in technology assisted
assessment are also discussed.
