The governance of physical and social connections

It is by no means a new idea that the world we live in is an interconnected one. Centuries before Castells’ seminal trilogy ‘The Information Age’, various European philosophers adopted a systemic view in order to explain certain physical and social phenomena. The 1950s were the heyday of total systems thinking: the idea that everything is connected to everything. This led to the assumption that planning and policy making should cov

Diversifying deep transitions:Accounting for socio-economic directionality

The paper sets out to enrich the emerging debate on ‘deep’, transversal transitions. It does so by drawing attention to socio-economic developments neglected in the Deep Transition (DT) framework of Kanger and Schot, such as marketization, labour contracts becoming more individual and precarious, and changing human beliefs, aspirations, needs and wants as important developments. The framework of Deep Transition is criticised for neglecting tensions and contestations about progress, the socio-economic order and distributional issues. This paper aims to complement ‘deep transitions’ research with insights about socio-economic transformation processes. These are shown to be conflict-ridden and full of tensions, creating pressures on socioeconomic orders and institutional logics. Because of this, development does not follow a neat pattern of convergence. In addition to identifying neglected issues and conceptual blind spots, the paper also outlines the scope for conceptual bridging between socio-technical and socio-economic transformation perspectives through attention to institutional logics and dialectics of change. We make a plea for a broader DT research agenda that covers relevant socio-economic rules, metaregimes and institutional contradictions. Attention to directionality helps to deal with three weaknesses of the DT framework: the assumption of convergence, materialism, and insufficient attention to the multitude of value orientations and logics

Business models for sustainable development: a process perspective

The process-oriented conceptualization of business models presented here resonates with the original formulation of sustainable development as a process. We draw on Actor-Network-Theory and Theories of Practices and illustrate with data on Bike-business models in the costal city of Ningbo China

Product Policy as an Instrument for Water Quality Management.

A main reason for the persistence of current water pollution lies in the diffuse character of many of its sources. For a large part such diffuse pollution is related to the production, use and waste of various kinds of products. For the reduction of this pollution, a product oriented policy strategy, based on interaction with stakeholders could be more successful than the traditional measures of direct regulation that were devised for point source reduction. In this article we identify different types of product policy, and explore the potential benefits and costs for water quality management. The methods that can be used in a product policy approach are illustrated with some examples. Although the specific advantages for water quality management have not been quantified yet, governments increasingly recognise the potential positive effects. In this context, the European Water Framework Directive, in stimulating product policy by enhancing public and stakeholders’ participation, can be considered to be part of a general development towards interactive water management

Building back normal? An investigation of practice changes in the charitable and on-the-go food provision sectors through COVID-19

The COVID-19 pandemic has brought about debates on rethinking food and other socio-technical systems. While swiftly re-establishing normality has understandable appeal in a crisis, the landscape-level changes during the pandemic also hold windows of opportunity to “build back better” and to achieve sustainability transitions. In this article, we ask whether a cycle of disruption and adaptation results either in the rise of more sustainable niche practices or the consolidation of the socio-technical regimes in place. To approach this question, we consider the specific cases of charitable and on-the-go food provision and examine the extent to which COVID-induced adaptations have resulted in debates about, and implementations of, more just and sustainable practices. We draw on systems transitions and practice theoretical approaches to elucidate dynamics and elasticity and thus the effect of socio-technical practice changes. After describing the pre-COVID food regimes, we evaluate organizational practice adaptations during the lockdowns with regard to (1) changing cultural images of food security and provision, (2) socio-technical innovations, and (3) new forms of governance. We find that rather than justifying the public and policy frame of “building back better,” the effect of recovery measures reinforces the socio-technical regimes and omits wider sectoral and societal sustainability challenges such as the systemic reduction of poverty and waste

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives