The public sector is actively pursuing digital transformation to ensure continuous operations and relevance. While existing research has outlined essential prerequisites for successful digital transformation, there is recognition of willful ignorance concerning these prerequisites. Public servants may in other words deliberately avoid understanding the necessary conditions for digital transformation, often driven by strategic motives such as evading responsibility and/or accountability. The phenomenon of willful ignorance constitutes an important yet under-researched area within the study of digital government. To close this gap, we investigate the latent factors of willful ignorance in public sector digital transformation, utilizing three sets of national panel data focused on digital transformation prerequisites. Employing exploratory factor analysis on an initial sample, we construct a factor model, subsequently assessing its validity through confirmatory factor analysis on two additional samples. Our research identifies and validates latent factors associated with willful ignorance in the digital transformation of the public sector. Building on these findings, we propose a mid-range variance theory termed “digital transformation decoupling”. By integrating this theory with existing knowledge, we present a set of propositions to guide future research in the realm of public sector digital transformation.

This study explores integrating generative AI to enhance citation context typing. Using Claude LLM, we generate synthetic data aligned with the Citation Typing Ontology (CiTO) to train a classifier. This supervised learning experiment involves training a classifier to identify citation types using this synthetic data. We evaluate the classifier’s performance on uncategorised citation statements. Additionally, we extend our analysis to test the classifier trained on English language citation context statements on statements extracted from Swedish and German research publications. A novel aspect of this work lies in the fusion of bibliometrics and experimental work in semantic modelling, employing language models to train machine learning models for research content evaluation. While acknowledging the inherent limitations of machine learning algorithms, we propose further testing using real-time scenarios and human evaluators. This study aims to push the boundaries of research methodology by integrating generative AI beyond text generation into the research process itself.

This article explores the broad and undefined research field of the social impact of the arts. The effects of art and culture are often used as justification for public funding, but the research on these interventions and their effects is unclear. Using a co-word analysis of over 10,000 articles published between 1990 and 2020, we examined the characteristics of the field as we have operationalised it through our searches. We found that since 2015 this research field has expanded and consists of different epistemologies and methodologies, summarised in largely overlapping subfields belonging to the social sciences, humanities, arts education, and arts and health/therapy. In formal or informal learning settings, studies of theatre/drama as an intervention to enhance skills, well-being, or knowledge among children are most common in our corpus. A study of the research front through the bibliographic coupling of the most cited articles in the corpus confirmed the co-word analysis and revealed new themes that together form the ground for insight into research on the social impact of the arts. This article can therefore inform discussions on the social value of culture and the arts. 

In this study, we have started to investigate how distinguishing the role of the cited reference from the subject of the cited reference can facilitate a more nuanced way to evaluate the citation context in the referring paper. Using natural language processing methods, we have developed methods to both enrich and distinguish specific traits in the aggregated citances. In future work we intend to extend the present analysis to a larger set of publications from the corpus and to cover more disciplines to be able to evaluate the results more precisely.

This work in progress aims to perform a comprehensive analysis of digital data publicly available from the Swedish parliament, such as motions, interpellations, and protocols from discussions in the plenum. To this end, we will use methods based on computational linguistics, such as topic modeling, word embeddings, and sentiment analysis, to identify prominent discourses corresponding to co-occurrence patterns of the words used. Using data from the early 1970s and onward, this project will involve a chronological examination of semantic and discursive changes with regard to topics such as equality, neutrality, the EU, the monarchy, immigration, climate change, and cultural policy. One research question that will be investigated in relation to this analysis is to what extent discursive changes can be detected within specific political parties, and what historical and political reasons can be posited to underlie these changes.Another research question focuses on scientometric aspects of how scholarly research is used to support claims made in the political discussion. With regard to this question, we will more specifically investigate the conceptual aspects of the texts surrounding citations (so-called citances) which will be mined for information such as what research is referred to in terms of individuals, position, disciplinary affiliations, and active research topics. In these citances, the significant content is often present as latent references that require further elucidation, together with an analysis of the sentiments expressed in the argumentation. This analysis will be further enhanced by investigating the usage of hedge terms that may indicate a level of uncertainty about the cited research.

Since many databases lack relevance ranking, a citation-based approach can be a valuable complement since it is possible to use citation-based data to indicate centrality, relevance, or visibility in the research community. However, using bibliometric methods in the humanities is often challenging since a lot of the research literature is not indexed in the traditional citation databases that we generally use for bibliometric mapping.

We introduce a combined bibliometric and semantic approach to extend a network of bibliographic records by incorporating a larger set of records lacking bibliometric features based on the semantic similarities between their titles. In order to expand the set of identified relevant articles, we used the Universal Sentence Encoder (USE) algorithm developed by Google Research to generate semantic vectors for the titles.

We searched several different databases, of which some include citation data, to create a pool C of candidate documents within the selected subject area. A set A of documents was obtained from a citation database to generate the initial network of articles. We then calculated the bibliographic coupling of articles as quantified by their shared references. 

We manually selected a small set S1 ⊂ A of documents representing different topical clusters as a seed for the expansion based on semantic similarities. For each document d ∈ S1, we ranked the documents in C in ascending order according to their cosine distance to the title vector assigned to d, then selecting the k documents closest to d. This procedure gave us a set S2 ⊂ C of documents to read. 

The results were evaluated using qualitative analysis to determine they were thematically relevant to the present information needs.