Can ChatGPT evaluate research quality?

Purpose: Assess whether ChatGPT 4.0 is accurate enough to perform research evaluations on journal articles to automate this time-consuming task. Design/methodology/approach: Test the extent to which ChatGPT-4 can assess the quality of journal articles using a case study of the published scoring guidelines of the UK Research Excellence Framework (REF) 2021 to create a research evaluation ChatGPT. This was applied to 51 of my own articles and compared against my own quality judgements. Findings: ChatGPT-4 can produce plausible document summaries and quality evaluation rationales that match the REF criteria. Its overall scores have weak correlations with my self-evaluation scores of the same documents (averaging r=0.281 over 15 iterations, with 8 being statistically significantly different from 0). In contrast, the average scores from the 15 iterations produced a statistically significant positive correlation of 0.509. Thus, averaging scores from multiple ChatGPT-4 rounds seems more effective than individual scores. The positive correlation may be due to ChatGPT being able to extract the author's significance, rigour, and originality claims from inside each paper. If my weakest articles are removed, then the correlation with average scores (r=0.200) falls below statistical significance, suggesting that ChatGPT struggles to make fine-grained evaluations. Research limitations: The data is self-evaluations of a convenience sample of articles from one academic in one field. Practical implications: Overall, ChatGPT does not yet seem to be accurate enough to be trusted for any formal or informal research quality evaluation tasks. Research evaluators, including journal editors, should therefore take steps to control its use. Originality/value: This is the first published attempt at post-publication expert review accuracy testing for ChatGPT.

Predicting article quality scores with machine learning: The UK Research Excellence Framework

National research evaluation initiatives and incentive schemes have previously chosen between simplistic quantitative indicators and time-consuming peer review, sometimes supported by bibliometrics. Here we assess whether artificial intelligence (AI) could provide a third alternative, estimating article quality using more multiple bibliometric and metadata inputs. We investigated this using provisional three-level REF2021 peer review scores for 84,966 articles submitted to the UK Research Excellence Framework 2021, matching a Scopus record 2014-18 and with a substantial abstract. We found that accuracy is highest in the medical and physical sciences Units of Assessment (UoAs) and economics, reaching 42% above the baseline (72% overall) in the best case. This is based on 1000 bibliometric inputs and half of the articles used for training in each UoA. Prediction accuracies above the baseline for the social science, mathematics, engineering, arts, and humanities UoAs were much lower or close to zero. The Random Forest Classifier (standard or ordinal) and Extreme Gradient Boosting Classifier algorithms performed best from the 32 tested. Accuracy was lower if UoAs were merged or replaced by Scopus broad categories. We increased accuracy with an active learning strategy and by selecting articles with higher prediction probabilities, as estimated by the algorithms, but this substantially reduced the number of scores predicted.

Artificial intelligence technologies to support research assessment: A review

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.

Can REF output quality scores be assigned by AI? Experimental evidence

This document describes strategies for using Artificial Intelligence (AI) to predict some journal article scores in future research assessment exercises. Five strategies have been assessed.

Pot, kettle: Nonliteral titles aren't science

Researchers may be tempted to attract attention through poetic titles for their publications, but would this be mistaken in some fields? Whilst poetic titles are known to be common in medicine, it is not clear whether the practice is widespread elsewhere. This article investigates the prevalence of poetic expressions in journal article titles 1996-2019 in 3.3 million articles from all 27 Scopus broad fields. Expressions were identified by manually checking all phrases with at least 5 words that occurred at least 25 times, finding 149 stock phrases, idioms, sayings, literary allusions, film names and song titles or lyrics. The expressions found are most common in the social sciences and the humanities. They are also relatively common in medicine, but almost absent from engineering and the natural and formal sciences. The differences may reflect the less hierarchical and more varied nature of the social sciences and humanities, where interesting titles may attract an audience. In engineering, natural science and formal science fields, authors should take extra care with poetic expressions, in case their choice is judged inappropriate. This includes interdisciplinary research overlapping these areas. Conversely, reviewers of interdisciplinary research involving the social sciences should be more tolerant of poetic license.

TensiStrength: Stress and relaxation magnitude detection for social media texts

Computer systems need to be able to react to stress in order to perform optimally on some tasks. This article describes TensiStrength, a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation, particularly in the context of transportation. It is slightly more effective than a comparable sentiment analysis program, although their similar performances occur despite differences on almost half of the tweets gathered. The effectiveness of TensiStrength depends on the nature of the tweets classified, with tweets that are rich in stress-related terms being particularly problematic. Although generic machine learning methods can give better performance than TensiStrength overall, they exploit topic-related terms in a way that may be undesirable in practical applications and that may not work as well in more focused contexts. In conclusion, TensiStrength and generic machine learning approaches work well enough to be practical choices for intelligent applications that need to take advantage of stress information, and the decision about which to use depends on the nature of the texts analysed and the purpose of the task.

Emotional Analysis of Blogs and Forums Data

We perform a statistical analysis of emotionally annotated comments in two large online datasets, examining chains of consecutive posts in the discussions. Using comparisons with randomised data we show that there is a high level of correlation for the emotional content of messages.