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Islam, modernity, and the human sciences
\"This book discloses a largely unnoticed dialogue between Muslim and Western social thought on the search for meaning and transcendence in the human sciences. The disclosure is accomplished by a comparative reading of contemporary Muslim debates on secular knowledge on the one hand, and of a foundational Western debate on the demise of metaphysics in the human sciences on the other hand. The comparative reading is grounded in a dialogical hermeneutic approach; that is, a hermeneutic approach to texts and cultural traditions that draws upon the work of Hans Georg Gadamer and also upon the insights of inter-religious dialogue\"-- Provided by publisher.
Book
Knowledge-intensive innovative entrepreneurship integrating Schumpeter, evolutionary economics, and innovation systems
This article proposes a novel conceptualization of knowledge-intensive innovative entrepreneurship, which can capture the main characteristics of a vital phenomenon in the modern economy. Our conceptualization is based upon the integration of Schumpeterian entrepreneurship, evolutionary economics, and innovation systems approach. It consists of a theoretical definition and a stylized process model. According to this view, knowledgeintensive innovative entrepreneurs are involved in the creation, diffusion, and use of knowledge; introduce new products and technologies; draw resources and ideas from their innovation system; and introduce change and dynamism into the economy. In the article, we also offer an empirical definition of knowledge-intensive innovative entrepreneurship, which we then use to identify its key characteristics and relevance. We conclude with recommendations for a future research agenda.
Journal Article
Emerging models of data governance in the age of datafication
The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe.
Journal Article
Natural language analyzed with AI-based transformers predict traditional subjective well-being measures approaching the theoretical upper limits in accuracy
We show that using a recent break-through in artificial intelligence –
transformers–
, psychological assessments from text-responses can approach theoretical upper limits in accuracy, converging with standard psychological rating scales. Text-responses use people's primary form of communication –
natural language
– and have been suggested as a more ecologically-valid response format than closed-ended rating scales that dominate social science. However, previous language analysis techniques left a gap between how accurately they converged with standard rating scales and how well ratings scales converge with themselves – a theoretical upper-limit in accuracy. Most recently, AI-based language analysis has gone through a transformation as nearly all of its applications, from Web search to personalized assistants (e.g., Alexa and Siri), have shown unprecedented improvement by using transformers. We evaluate transformers for estimating psychological well-being from questionnaire text- and descriptive word-responses, and find accuracies converging with rating scales that approach the theoretical upper limits (Pearson
r
= 0.85,
p
< 0.001,
N
= 608; in line with most metrics of rating scale reliability). These findings suggest an avenue for modernizing the ubiquitous questionnaire and ultimately opening doors to a greater understanding of the human condition.
Journal Article
Aligning research with policy and practice for sustainable agricultural land systems in Europe
Agriculture is widely recognized as critical to achieving the Sustainable Development Goals (SDGs), but researchers, policymakers, and practitioners have multiple, often conflicting yet poorly documented priorities on how agriculture could or should support achieving the SDGs. Here, we assess consensus and divergence in priorities for agricultural systems among research, policy, and practice perspectives and discuss the implications for research on trade-offs among competing goals. We analyzed the priorities given to 239 environmental and social drivers, management choices, and outcomes of agricultural systems from 69 research articles, the SDGs and four EU policies, and seven agricultural sustainability assessment tools aimed at farmers. We found all three perspectives recognize 32 variables as key to agricultural systems, providing a shared area of focus for agriculture’s contribution to the SDGs. However, 207 variables appear in only one or two perspectives, implying that potential trade-offs may be overlooked if evaluated from only one perspective. We identified four approaches to agricultural land systems research in Europe that omit most of the variables considered important from policy and practice perspectives. We posit that the four approaches reflect prevailing paradigms of research design and data analysis and suggest future research design should consider including the 32 shared variables as a starting point for more policy- and practice-relevant research. Our identification of shared priorities from different perspectives and attention to environmental and social domains and the functional role of system components provide a concrete basis to encourage codesigned and systems-based research approaches to guide agriculture’s contribution to the SDGs.
Journal Article
Why residual emissions matter right now
Net-zero targets imply that continuing residual emissions will be balanced by carbon dioxide removal. However, residual emissions are typically not well defined, conceptually or quantitatively. We analysed governments’ long-term strategies submitted to the UNFCCC to explore projections of residual emissions, including amounts and sectors. We found substantial levels of residual emissions at net-zero greenhouse gas emissions, on average 18% of current emissions for Annex I countries. The majority of strategies were imprecise about which sectors residual emissions would originate from, and few offered specific projections of how residual emissions could be balanced by carbon removal. Our findings indicate the need for a consistent definition of residual emissions, as well as processes that standardize and compare expectations about residual emissions across countries. This is necessary for two reasons: to avoid projections of excessive residuals and correspondent unsustainable or unfeasible carbon-removal levels and to send clearer signals about the temporality of fossil fuel use.Residual emissions, as a noticeable component of net-zero plans, should be analysed transparently and with specificity. By examining the national long-term strategies, the authors find that currently residual emissions are not clearly defined and are unlikely to be balanced by land-based carbon removal.
Journal Article
Towards a relational paradigm in sustainability research, practice, and education
Relational thinking has recently gained increasing prominence across academic disciplines in an attempt to understand complex phenomena in terms of constitutive processes and relations. Interdisciplinary fields of study, such as science and technology studies (STS), the environmental humanities, and the posthumanities, for example, have started to reformulate academic understanding of nature-cultures based on relational thinking. Although the sustainability crisis serves as a contemporary backdrop and in fact calls for such innovative forms of interdisciplinary scholarship, the field of sustainability research has not yet tapped into the rich possibilities offered by relational thinking. Against this background, the purpose of this paper is to identify relational approaches to ontology, epistemology, and ethics which are relevant to sustainability research. More specifically, we analyze how relational approaches have been understood and conceptualized across a broad range of disciplines and contexts relevant to sustainability to identify and harness connections and contributions for future sustainability-related work. Our results highlight common themes and patterns across relational approaches, helping to identify and characterize a relational paradigm within sustainability research. On this basis, we conclude with a call to action for sustainability researchers to codevelop a research agenda for advancing this relational paradigm within sustainability research, practice, and education.
Journal Article
Refugee entrepreneurship: systematic and thematic analyses and a research agenda
Refugee entrepreneurship has recently entailed increased scholarly mobilization and drastic growth in the volume of salient scientific research. However, this emerging research stream is marked by fragmentation and incoherence, primarily due to the multidisciplinary and context-specific nature of its extant findings. While this process is natural for emerging fields, the current state of research necessitates a comprehensive review, synthesis, and organization of its subject matter. Hence, this study systematically and thematically explores the landscape of refugee entrepreneurship research and its intellectual territory across diverse disciplines to take stock of a repository of the literature and trace its emergence, nature, and development. By analyzing 131 publications, this paper thus lays a collective research foundation for building a coherent theory, making incremental adjustments, and forming the ontological and epistemological basis for refugee entrepreneurship research. The study also identifies gaps in the literature and opens pathways for future scholarly endeavors.Plain English SummaryRefugee entrepreneurship is an intriguing topic, providing a unique perspective for exploring the link between experiencing disruptive life events caused by being forced to leave one’s homeland and founding a new business in an unplanned country of resettlement. Refugee entrepreneurship has been of recent interest to researchers due to its potential to alleviate the grand socioeconomic challenges triggered by the “refugee crisis” of mid-2010s. Vigorous scholarly engagement has generated many publications on the topic. However, refugee entrepreneurship is not a well-developed research area because current knowledge is scattered across different fields, and there exists no unified conceptualization to understand refugee entrepreneurship activities. Hence, this study makes a comprehensive analysis and organization of its subject matter to create a common academic basis for future research. The principal implication of this study is that the scope for designing better refugee-integration policies should also involve a nuanced understanding of refugee entrepreneur/ship.
Journal Article
Human-machine-learning integration and task allocation in citizen science
The field of citizen science involves the participation of citizens across different stages of a scientific project; within this field there is currently a rapid expansion of the integration of humans and AI computational technologies based on machine learning and/or neural networking-based paradigms. The distribution of tasks between citizens (“the crowd”), experts, and this type of technologies has received relatively little attention. To illustrate the current state of task allocation in citizen science projects that integrate humans and computational technologies, an integrative literature review of 50 peer-reviewed papers was conducted. A framework was used for characterizing citizen science projects based on two main dimensions: (a) the nature of the task outsourced to the crowd, and (b) the skills required by the crowd to perform a task. The framework was extended to include tasks performed by experts and AI computational technologies as well. Most of the tasks citizens do in the reported projects are well-structured, involve little interdependence, and require skills prevalent among the general population. The work of experts is typically structured and at a higher-level of interdependence than that of citizens, requiring expertize in specific fields. Unsurprisingly, AI computational technologies are capable of performing mostly well-structured tasks at a high-level of interdependence. It is argued that the distribution of tasks that results from the combination of computation and citizen science may disincentivize certain volunteer groups. Assigning tasks in a meaningful way to citizen scientists alongside experts and AI computational technologies is an unavoidable design challenge.
Journal Article
Co-existing Notions of Research Quality
Notions of research quality are contextual in many respects: they vary between fields of research, between review contexts and between policy contexts. Yet, the role of these co-existing notions in research, and in research policy, is poorly understood. In this paper we offer a novel framework to study and understand research quality across three key dimensions. First, we distinguish between quality notions that originate in research fields (Field-type) and in research policy spaces (Space-type). Second, drawing on existing studies, we identify three attributes (often) considered important for 'good research': its originality/novelty, plausibility/reliability, and value or usefulness. Third, we identify five different sites where notions of research quality emerge, are contested and institutionalised: researchers themselves, knowledge communities, research organisations, funding agencies and national policy arenas. We argue that the framework helps us understand processes and mechanisms through which 'good research' is recognised as well as tensions arising from the co-existence of (potentially) conflicting quality notions. (HRK / Abstract übernommen).
Journal Article