A comparative study on characteristics of retracted publications across different open access levels

Abstract:  Purpose: Recently, global science has shown an increasing open trend, however, the characteristics of research integrity of open access (OA) publications have rarely been studied. The aim of this study is to compare the characteristics of retracted articles across different OA levels and discover whether OA level influences the characteristics of retracted articles.

Design/methodology/approach: The research conducted an analysis of 6,005 retracted publications between 2001 and 2020 from the Web of Science and Retraction Watch databases. These publications were categorized based on their OA levels, including Gold OA, Green OA, and non-OA. The study explored retraction rates, time lags and reasons within these categories.

Findings: The findings of this research revealed distinct patterns in retraction rates among different OA levels. Publications with Gold OA demonstrated the highest retraction rate, followed by Green OA and non-OA. A comparison of retraction reasons between Gold OA and non-OA categories indicated similar proportions, while Green OA exhibited a higher proportion due to falsification and manipulation issues, along with a lower occurrence of plagiarism and authorship issues. The retraction time lag was shortest for Gold OA, followed by non-OA, and longest for Green OA. The prolonged retraction time for Green OA could be attributed to an atypical distribution of retraction reasons.

Research limitations: There is no exploration of a wider range of OA levels, such as Hybrid OA and Bronze OA.

Practical implications: The outcomes of this study suggest the need for increased attention to research integrity within the OA publications. The occurrences of falsification, manipulation, and ethical concerns within Green OA publications warrant attention from the scientific community.

Originality/value: This study contributes to the understanding of research integrity in the realm of OA publications, shedding light on retraction patterns and reasons across different OA levels.