All linguistic research has the potential to reproduce or challenge racial notions.

—Linguistic Society of America Statement on Race (2019)

The LSA Statement on Race stems from a larger conversation around undertheorized treatment of race and ethnicity in linguistics research and practice. In this commentary, we define racial identity and ethnicity and explain their relevance for linguistic research. We discuss considerations that linguistic researchers should take prior to research, during study design, and following research, and we offer specific recommendations when soliciting or using race and ethnicity data. These recommendations aim to help researchers avoid social harm, ensure ethical compliance and research integrity, and improve descriptive accuracy, especially for undersampled groups, by balancing research transparency with generalizability. We consider issues germane to collecting self-disclosures of ethnicity and racial identity in a range of study types spanning several subfields of linguistics. We give concrete examples of questions that may arise in planning studies in computational and corpus-based linguistics, formal linguistics, experimental linguistics, and qualitative linguistics. We speak to ethical considerations, including the importance of using locally constructed labels, analyst positionality, and respect for communities. Our goals are to provide linguistic researchers with a firmer basis for conceptualizing racial identity and ethnicity particularly as pertains to linguistics, and to supply a guide that aids linguists in reflecting on their own study design, positionality, and responsibility to participants and communities.

Effective data management is essential for tasks involving decisions based on data, including knowledge synthesis and literature reviews. Despite this, how to carry out data management in literature reviews effectively remains unclear. With the increasing volume of research papers and the expansion of computational techniques for processing data (e.g., machine learning or large language models), it becomes imperative to consider data management as a crucial element for the advancement of literature review practices and tools. Presently, there are shortcomings related to (1) handling the growth of research to be synthesized, (2) addressing data quality issues when applying computational techniques or facilitating the verification of content produced by generative artificial intelligence, (3) enabling efficient reuse of datasets and innovative recombination of tools, and (4) facilitating transparent collaboration across heterogeneous review teams. To address these shortcomings, we develop the C5-DM Framework with conceptual principles to address data management challenges across five areas relevant to literature reviews: data conceptualization, collection, curation, control, and consumption. Methodological guidance for researchers with respect to these five areas is necessary to reduce errors, save time on repetitive tasks, and allow review teams to develop insightful syntheses.

What does conducting law and society scholarship have anything to do with wilting blooms? In this essay, Lynette J. Chua makes the connection between the two through her reminder to law and society scholars to study the taken-for-granted, an intellectual project that has become all the more urgent as politicians and activists contest concepts such as citizenship, gender, territories, religion and rights. She also calls upon fellow law and society scholars to be humble – for the significance and impact of our research, like flowers, could come and go with the seasons.

Research syntheses have demonstrated that pronunciation instruction works, which means that whether instruction is effective is no longer an open question. Instead, contemporary intervention research has shifted to investigating how instruction can be further optimized, asking targeted questions about the instructional features that catalyze learning. In this paper, I examine the concept of instructional optimization, focusing on anticipated effect sizes (gains). I outline a four-pronged empirical approach to provide robust data for designing optimal pronunciation interventions. First, I describe the need for replication studies, which provide insight into the precision and stability of effects across distinct research samples and contexts. Second, I advocate for a systematic approach to study design. In such an approach, which is closely tied to the principles of replication, one or two variables are manipulated at a time, leading to a set of maximally comparable studies that lend insight into the impact of specific variables. Third, I explain the need to situate instruction within a longitudinal perspective to examine how robust and durable instructional gains are. Finally, I turn to adaptive approaches, where the surface format that instruction takes is highly variable and responsive to learner needs while the adaptive decision tree that generates the form is fixed and replicable.

This study offers a systematic and theory-informed integrative synthesis of research at the intersection of artificial intelligence (AI) and entrepreneurship. Although interest in this domain has expanded rapidly, existing research remains fragmented, technology centered, and weakly connected to theories of entrepreneurial decision-making. To address this gap, the study adopts a hybrid review design that combines a systematic literature review with bibliometric co-word analysis and thematic synthesis. Based on 372 articles indexed in the Web of Science (WoS) Core Collection (2010–2025), the analysis maps the intellectual structure, thematic landscape, and temporal evolution of AI–entrepreneurship research. Four thematic quadrants are identified, reflecting core applications, transversal foundations, isolated specializations, and peripheral themes. The synthesis shows that AI is largely conceptualized as a functional input, while cognitive and behavioral dimensions of entrepreneurial judgment remain marginal. Building on these insights, the article proposes a cognitively informed research agenda to guide future work.

This article discusses the challenges of moving towards student-centredness in East-Central Europe through the example of Hungary’s subject-focused academic culture and the (re-)design of a political science research methods course at the University of Szeged for Spring 2012. Although countries participating in the Bologna Process undersigned the importance of student-centredness, few countries have actually yet moved in this direction. In addition, we know very little about how these instructional methods work outside the Western democratic context. I show that research into teaching is an important means to improve the process of education and that there are specific problems in transferring student-centredness into post-Communist higher education settings. Finally, I argue that knowing one’s teaching context is vital for planning student-centred courses effectively, which would be greatly fostered by experiencing other teaching contexts through early-career teacher exchanges. The European Commission has recently affirmed its commitment to staff exchanges, but such opportunities are only likely to be beneficial if they go beyond the current 6-week long exchange scheme that the Erasmus programme offers.

‘Is political science out of step with the world?’ This question, raised by John E. Trent in a recent issue, is part of a recurring debate about the development of our discipline. In that article, and in a subsequent book with his colleague Michael Stein, John Trent blames adherents of the ‘scientific method’ for political science’s growing irrelevance. We challenge this claim by arguing that Trent falls back on outdated polarities between ‘objective and normative’, and ‘explanation and interpretation’, in order to justify his allegation. We argue for the need to review our methods continuously, rather than dig up a fruitless and biased division between qualitative and quantitative approaches.

In its rational, organisational, historical and discursive varieties, the new institutionalism research agenda is arguably the most successful paradigm in comparative politics and public policy analysis. However, neo-institutional practice applied to comparative policy analysis reveals four pitfalls, that is, ‘institutional determinism’, ‘drop in the box’, ‘second best residual explanations’ and ‘theoretical conjectures without foundational mechanisms’. We illustrate and examine the pitfalls and consider the conceptual and methodological implications for the comparative analysis of policies. In the conclusion, we present options for rescuing institutional analysis from bad practice.

Research on interest groups has evolved from a focus on small-N studies to larger-N studies in the past 15 years. While both European and American research has become more sophisticated and aware of methodological aspects, there is yet no specialized literature on methods regarding how to study interest groups. Only few studies discuss the methodological implications of interest group studies, as well as the transferability of methods employed in other areas of political science to this research area. The contributions in this symposium focus on major problems and topics in interest group research and elaborate methods to deal with them: (1) the identification of the relevant interest group population, (2) the analysis of interest group strategies such as access, (3) the identification of interest groups positions and frames, and (4) the measurement of interest group success and influence. The introduction outlines these research problems and describes how the contributions to this symposium address them. The aim of the symposium is to increase awareness of the intricacies of these research problems, outline suitable practices to handle them, and stimulate debate on these methodological aspects.

Indigenous communities have historically been some of the most researched communities around the globe. But much of this research has caused great harm to Indigenous peoples. In response to these harmful and abusive research practices, Indigenous leaders and scholars have envisioned new research principles, practices, methodologies, and policies that center Indigenous peoples, values, worldviews, governance and knowledge systems: Indigenous data sovereignty. This article examines the current state of third sector research relating to Indigenous peoples. We find that Indigenous communities are largely absent from third sector research and there are significant issues with how third sector research conceptualizes Indigenous peoples. We introduce Indigenous data sovereignty as a demand and framework of Indigenous communities aimed at supporting more equitable research practices and pathways to advance research with and for—and not on—Indigenous communities.

This article presents a new strategy for reviewing large, multidisciplinary academic literatures: a multi-method comprehensive review (MCR). We present this approach and demonstrate its use by the NGO Knowledge Collective, which aims to aggregate knowledge on NGOs in international development. We explain the process by which scholars can identify, analyze, and synthesize a population of hundreds or thousands of articles. MCRs facilitate cross-disciplinary synthesis, systematically identify gaps in a literature, and can create data for further scholarly use. The main drawback is the significant resources needed to manage the volume of text to review, although such obstacles may be mitigated through advances in “big data” methodologies over time.

With the growth of third sector research, the field needs more dedicated discussion on how we study the third sector, not only the decisions in research design or data collection process but also the general research approaches and the way we analyze the data. In this introduction to the special issue of Voluntas Volume I, we discuss how the sector can foster a more inclusive and diverse research community for people, topics, and methods. We also discuss the implications of methodological pluralism, an organizing principle of a research community that fosters respect, appreciation, and empathy between its members. We conclude by calling for more empathetic, transparent, and accountable research.