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This chapter argues that the primary reason for the underdeveloped state of microfoundations research in language and communications studies is methodological. It asserts that the long-standing methodological division between micro and macro analyses (traditionally small-scale and large-scale, respectively) has led to their continued theoretical separation. The paper draws from Giddens’ theory of structuration and newly developed computational methods to outline an alternative, mixed-methods framework for discourse analysis that the author calls Recursive Analysis (RA). The author demonstrates the application of the RA framework through a case study of the electric vehicle industry that aligns small-scale and large-scale textual analysis to generate theoretical insights.

Much of contemporary institutional theory rests on the identification of structured, coherent, and encompassing logics, and from there proceeds to examine multilevel dynamics or the relationship between logics in a field. Less research directly studies the internal properties and dynamics of logics and how they are structured over time. In this paper, we propose a method for understanding the content and organization of logics over time. We advocate for an analysis of logics that is grounded in a repertoire view of culture (Swidler, 1986; Weber, 2005). This approach involves identifying the set of cultural categories that can make up logics, and measuring empirically the dimensions that mark a cultural system as more or less logic-like. We discuss several text analytic approaches suitable for discourse data, and outline a seven-step method for describing the internal organization of a cultural repertoire in term of its “logic-ness.” We provide empirical illustrations from a historical analysis of the field of alternative livestock agriculture. Our approach provides an integrated theoretical and methodological framework for the analysis of logics across a range of settings.

Much of contemporary institutional theory rests on the identification of structured, coherent, and encompassing logics, and from there proceeds to examine multilevel dynamics or the relationship between logics in a field. Less research directly studies the internal properties and dynamics of logics and how they are structured over time. In this paper, we propose a method for understanding the content and organization of logics over time. We advocate for an analysis of logics that is grounded in a repertoire view of culture (Swidler, 1986; Weber, 2005). This approach involves identifying the set of cultural categories that can make up logics, and measuring empirically the dimensions that mark a cultural system as more or less logic-like. We discuss several text analytic approaches suitable for discourse data, and outline a seven-step method for describing the internal organization of a cultural repertoire in term of its “logic-ness.” We provide empirical illustrations from a historical analysis of the field of alternative livestock agriculture. Our approach provides an integrated theoretical and methodological framework for the analysis of logics across a range of settings.

Social movement scholars use protest events as a way to quantify social movements and have most often used large, national newspapers to identify those events. This has introduced known and unknown biases into our measurement of social movements. We know that national newspapers tend to cover larger and more contentious events and organizations. Protest events are furthermore a small part of what social movements actually do. Without other readily available options to quantify social movements, however, big-N studies have continued to focus on protest events via a few large newspapers. With advances in digitized data and computational methods, we now no longer have to rely on large newspapers or focus only on protests to quantify important aspects of social movements. In this paper, we use the environmental movement as a case study, analyzing data from a wide range of local, regional, and national newspapers in the United States to quantify multiple facets of social movements. We argue that the incorporation of more data and new methods to quantify information in text has the potential to transform the way we both conceive of and measure social movements in three ways: (1) the type of focal social movement organization included, (2) the type of tactics and issues covered, and (3) the ability to go beyond protest events as the primary unit of analysis. In addition to demonstrating ways that the focus on counting protest events has introduced specific biases in the type of tactics, issues, and organizations covered in social movement research, we argue that computational methods can help us extract and count meaningful aspects of social movements well beyond event counts. In short, the infusion of new data and methods into social movements, peace, and conflict studies could lead us to a substantial shift in the way we quantify social movements, from protest events to everything that occurs outside of them.

Cybersecurity competitions can effectively develop skills, but engaging a wide learner spectrum is challenging. This study aims to investigate the perceptions of cybersecurity competitions among Reddit users. These users constitute a substantial demographic of young individuals, often participating in communities oriented towards college students or cybersecurity enthusiasts. The authors specifically focus on novice learners who showed an interest in cybersecurity but have not participated in competitions. By understanding their views and concerns, the authors aim to devise strategies to encourage their continuous involvement in cybersecurity learning. The Reddit platform provides unique access to this significant demographic, contributing to enhancing and diversifying the cybersecurity workforce.

The authors propose to mine Reddit posts for information about learners’ attitudes, interests and experiences with cybersecurity competitions. To mine Reddit posts, the authors developed a text mining approach that integrates computational text mining and qualitative content analysis techniques, and the authors discussed the advantages of the integrated approach.

The authors' text mining approach was successful in extracting the major themes from the collected posts. The authors found that motivated learners would want to form a strategic way to facilitate their learning. In addition, hope and fear collide, which exposes the learners’ interests and challenges.

The authors discussed the findings to provide education and training experts with a thorough understanding of novice learners, allowing them to engage them in the cybersecurity industry.

The paper examines academic historians' information interactions with material from digital historical-newspaper collections as the research process unfolds.

The study employed qualitative analysis from in-depth interviews with Finnish history scholars who use digitised historical newspapers as primary sources for their research. A model for task-based information interaction guided the collection and analysis of data.

The study revealed numerous information interactions within activities related to task-planning, the search process, selecting and working with the items and synthesis and reporting. The information interactions differ with the activities involved, which call for system support mechanisms specific to each activity type. Various activities feature information search, which is an essential research method for those using digital collections in the compilation and analysis of data. Furthermore, application of quantitative methods and multidisciplinary collaboration may be shaping culture in history research toward convergence with the research culture of the natural sciences.

For sustainable digital humanities infrastructure and digital collections, it is of great importance that system designers understand how the collections are accessed, why and their use in the real-world context. The study enriches understanding of the collections' utilisation and advances a theoretical framework for explicating task-based information interaction.

Field emergence poses an intriguing problem for institutional theorists. New issue fields often arise at the intersection of different sectors, amidst extant structures of meanings and actors. Such nascent fields are fragmented and lack clear guides for action; making it unclear how they ever coalesce. The authors propose that provisional social structures provide actors with macrosocial presuppositions that shape ongoing field-configuration; bootstrapping the field. The authors explore this empirically in the context of social impact investing in the UK, 2000–2013, a period in which this field moved from clear fragmentation to relative alignment. The authors combine different computational text analysis methods, and data from an extensive field-level study, to uncover meaningful patterns of interaction and structuration. Our results show that across various periods, different types of actors were linked together in discourse through “actor–meaning couplets.” These emergent couplings of actors and meanings provided actors with social cues, or macrofoundations, which guided their local activities. The authors thus theorize a recursive, co-constitutive process: as punctuated moments of interaction generate provisional structures of actor–meaning couplets, which then cue actors as they navigate and constitute the emerging field. Our model re-energizes the core tenets of new structuralism and contributes to current debates about institutional emergence and change.

A lot has been discussed about Peter Drucker, and there exists significant written content admiring or criticizing his work as a management writer. This paper aims to offer a holistic analysis of Peter Drucker’s written contributions to better understand his views of society, government and organizations of all kinds.

Many have written about Peter Drucker and his considerable impact on the practical and philosophical foundations of modern management. Yet, there has been no systematic scholarly evaluation of Drucker as a writer, although many have praised and criticized his written work on management. In this study, the authors offer an analysis of Peter Drucker’s written contributions to evaluate his central contributions, as well as how he communicated his ideas on society and management.

A comprehensive analysis of Drucker’s word usage and writing style throughout his writing career forms an evidence-based approach to better understand his viewpoints and objectively evaluate the criticisms surrounding his work.

This research contributes to a better understanding of Peter Drucker’s central contributions, concerns and sentiments, as it relates to not only business management but also to his views of society, government and organizations of all kinds. A reconsideration of Drucker as a writer presents possible implications for the practice of management.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

The purpose of this paper is to discuss a Multi-perspective approach to knowledge production in terms of a set of cybernetic concepts relevant to the approach; to describe a software system that computationally embodies the approach; and to articulate a research project that pragmatically employs the approach.

A definition is provided. The paper uses a survey methodology, exploring relevant cybernetic and contemporary technological concepts. An operational software mechanism (The Insight Engine) is discussed that enables the bridging of transdisciplinary concepts by a user in the service of accretive research –Recombinant Informatics.

Many cybernetic concepts are relevant to contemporary research into cognition and Neosentience research. More study needs to be undertaken related to historical BCL projects in terms of articulating relevance to contemporary research.

Future research seeks to extend the computational functionality of “The insight engine”, as well as uncover relevant BCL/cybernetic materials.

The software is unique in the field and already there is interest in its use by differing research communities including the Duke Institute for Brain Sciences, and at Stanford, research under Ian Hodder.

The Insight Engine has potential to be used as a multi-perspective tool for many different fields enabling different forms of distributed, transdisciplinary team-based research.

This text is valuable to researchers interested in new forms of interface, augmentation of thought and learning via computational approaches; and the development of bridges between novel research areas, including contemporary, historical BCL, and other cybernetic inquiry.