Metadata Enrichment of Long Text Documents using Large Language Models

In this project, we semantically enriched and enhanced the metadata of long text documents, theses and dissertations, retrieved from the HathiTrust Digital Library in English published from 1920 to 2020 through a combination of manual efforts and large language models. This dataset provides a valuable resource for advancing research in areas such as computational social science, digital humanities, and information science. Our paper shows that enriching metadata using LLMs is particularly beneficial for digital repositories by introducing additional metadata access points that may not have originally been foreseen to accommodate various content types. This approach is particularly effective for repositories that have significant missing data in their existing metadata fields, enhancing search results and improving the accessibility of the digital repository.

"It answers questions that I didn't know I had": Ph.D. Students' Evaluation of an Information Sharing Knowledge Graph

Interdisciplinary PhD programs can be challenging as the vital information needed by students may not be readily available, it is scattered across university's websites, while tacit knowledge can be obtained only by interacting with people. Hence, there is a need to develop a knowledge management model to create, query, and maintain a knowledge repository for interdisciplinary students. We propose a knowledge graph containing information on critical categories and their relationships, extracted from multiple sources, essential for interdisciplinary PhD students. This study evaluates the usability of a participatory designed knowledge graph intended to facilitate information exchange and decision-making. The usability findings demonstrate that interaction with this knowledge graph benefits PhD students by notably reducing uncertainty and academic stress, particularly among newcomers. Knowledge graph supported them in decision making, especially when choosing collaborators in an interdisciplinary setting. Key helpful features are related to exploring student faculty networks, milestones tracking, rapid access to aggregated data, and insights into crowdsourced fellow students' activities. The knowledge graph provides a solution to meet the personalized needs of doctoral researchers and has the potential to improve the information discovery and decision-making process substantially. It also includes the tacit knowledge exchange support missing from most current approaches, which is critical for this population and establishing interdisciplinary collaborations. This approach can be applied to other interdisciplinary programs and domains globally.

The Impact of AI on Academic Research and Publishing

Generative artificial intelligence (AI) technologies like ChatGPT, have significantly impacted academic writing and publishing through their ability to generate content at levels comparable to or surpassing human writers. Through a review of recent interdisciplinary literature, this paper examines ethical considerations surrounding the integration of AI into academia, focusing on the potential for this technology to be used for scholarly misconduct and necessary oversight when using it for writing, editing, and reviewing of scholarly papers. The findings highlight the need for collaborative approaches to AI usage among publishers, editors, reviewers, and authors to ensure that this technology is used ethically and productively.

Thanking the World: Exploring Gender-Based Differences in Acknowledgment Patterns and Support Systems in Theses

Research on acknowledgment sections of scientific papers has gained significant attention, but there remains a dearth of studies examining acknowledgments in the context of Electronic Theses and Dissertations. This paper addresses this gap by investigating the sources of support for male and female researchers in completing their master's or doctoral theses, focusing on the discipline of Library and Information Science. We utilize a novel method of extracting the various types of support systems that are acknowledged in 1252 ETDs using RoBERTa-based models. The most prominent forms of support acknowledged by researchers are academic, moral, financial, and religious support. While there are no significant gender-based differences in religious and financial support, the ratio of academic to moral support acknowledged by researchers shows strong gender-based variation. Additionally, advisors display a preference for supervising same-gender researchers. By comprehending the nuances of support systems and the unique challenges faced by researchers of different genders, we can foster a more inclusive and supportive academic environment. The insights gained from this research have implications for improving mentoring practices and promoting gender equality in academia.