This study examines the computational problem-solving capabilities of GPT-4, focusing on its knowledge of machine learning, email categorization, and computational problem solving, alongside its proficiency in Python programming, computational abstraction, and program debugging. The aim of these investigations is to evaluate whether the capabilities of Large Language Models (LLMs), as demonstrated by GPT-4, can support Master of Library and Information Science (MLIS), graduate students in developing computational thinking skills relevant to digital archival tasks.

This paper describes an investigation of GPT-4’s knowledge in some areas of archival practice, and its ability to think computationally about archival tasks. It is demonstrated that GPT-4 has shown an understanding of ten among the twenty-two distinct forms of computational thinking. When GPT-4 is combined with plugins, it is able to apply some of these methods and tools to digital archival tasks.

This paper describes how methods of natural language processing, grammatical description and parsing can be uses to recognize the document types of records distributed by the White House Press Office. It is also described how Dublin Core metadata can be extracted from these records to improve access to those records via Faceted Search. The applications being developed have broad potential use in the Presidential Libraries. Research issues being explored include automatic induction of grammars for defining document types and automatic methods for identifying related presidential records.

This paper explores whether the computational thinking practices of mathematicians and scientists in the physical and biological sciences are also the practices of archival scientists. It is argued that these practices are essential elements of an archival science education in preparing students for a professional archival career.

We describe computational treatments of archival collections through a case study involving World War II Japanese-American Incarceration Camps. We focus on automating the detection of personally identifiable information or PII. The paper also discusses the emergence of computational archival science (CAS) and the development of a computational framework for library and archival education. Computational Thinking practices are applied to Archival Science practices. These include: (1) data creation, manipulation, analysis, and visualization (2) designing and constructing computational models, and (3) computer programming, developing modular computational solutions, and troubleshooting and debugging. We conclude with PII algorithm accuracy, transparency, and performance considerations and future developments.

The discipline of professional archivists is rapidly changing. Most contemporary records are created, stored, maintained, used and preserved in digital form. Most graduate programs and continuing education programs in Archival Studies address this challenge by introducing students to information technology as it relates to digital records. We propose an approach to addressing this challenge based on introducing computational thinking into the graduate archival studies curriculum.

This paper describes the linguistic analysis of index note cards from record series of the World War II Japanese-American Internment Camps that are in the custody of the National Archives. It also describes the use of GATE Developer, and an extension of ANNIE, a GATE plugin, in linguistic processing of information specific to index note cards in order to extract metadata supporting access and archival decisions regarding record release and withdrawal. The content of the index cards will be interpreted as OWL/RDF statements. Those statements will be stored in a graph database and used with objects such as digital maps and photos to produce an interactive user interface to exhibit events at relocation centers.