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Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue.

In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data.

The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles.

The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).

The goal of this chapter is to introduce the network paradigm for analyzing relational phenomena and organizing knowledge in higher education research. This introduction is presented by example: it analyzes knowledge on inter-organizational relationships of higher education institutions. The formation, maintenance, and impact of relationships are implicitly relational, although they have largely been understood as a consequence of institutional practices. Through the network paradigm, we show that focusing on relations we can develop new and more precise models to understand the antecedents, consequences, and characteristics of these networks.

Shortages of personal protective equipment (PPE) across the United States at the start of the COVID-19 pandemic created entrepreneurial opportunities for sewists and makers. In the United States in March and April 2020, masks were not readily available to the general public from existing retailers and PPE for medical use was being rationed for healthcare workers. Sewists and crafters, professionals and amateurs alike, began making and selling and/or donating masks. For individuals with sewing skills and time, sewing and selling masks became a lifeline financially, personally, and socially. To understand the experiences of people who made and distributed handmade masks during the early months of the pandemic in the United States, an interdisciplinary team developed an online cross-sectional survey instrument using a qualitative-dominated approach with both open and closed questions. This chapter explores themes identified from a sample of 94 participants, predominantly female-identifying, who created an enterprise or added a product line to an existing business. The sample includes individuals who did not identify as a ‘creative entrepreneur’ prior to the pandemic but did identify as an entrepreneur after starting a mask-making venture. Informed by entrepreneurship literature, the authors observed that these nascent entrepreneurs articulated recognisable motivations for social entrepreneurship, showed signs of pre-existing entrepreneurial mindsets, and employed business models and marketing tactics of entrepreneurs, largely without any business training. Implications for the study include increased recognition of latent entrepreneurial readiness, interest of women in social entrepreneurship, and higher levels of business knowledge among women than previously recognised.