Actor-network Research Articles

Machine (network) learning in K-12 classrooms

In times when machine learning (ML) and other artificial intelligence (AI) technologies are expanding the role and definition of network learning in schools, this short paper reports from a practice-centred research project that explores how K-12 teachers affect and are affected by educational technologies with AI. Accelerated by the COVID-19 pandemic, data-driven and decision-making systems with ML are already entering various educational policy and practice realms, often underpinned by promises of automation and personalization. A growing number of research, drawing from the theoretical orientations and empirical approaches from Science & Technology Studies is increasingly unpacking such promises as well as addressing controversies directly related to the constitutions of ML AI in education. Still, little research explores the adoption of data-driven AI technologies in classrooms from a socio-material, networked learning stance. This short paper introduces such work (in progress) drawing on ethnographic fieldwork conducted in Sweden. Guided by the ontological and methodological approaches of Actor-Network-Theory (ANT), the study focuses on the interactions in K-12 classrooms between commercial ML technologies and teachers. Methodologically this means engaging with both human and non-human actors through ethnographic approaches striving for very specific descriptions of interactions within the actor-network and its enacted realities. Preliminary findings from the first of two envisaged case studies in which a ML-based teaching aid in mathematics was tried out in 22 classrooms indicate how compensatory and contradictory actions and accounts emerge within the network of heterogeneous actors. Human actors seem to compensate for the algorithmic actions of the specific educational technology with ML. This is however not a fait accompli but a continuous and unsettled process in the making between humans and the (non-human) technology. Preliminary results also suggest how controversies of ML algorithms in teaching aids, such as their lack of transparency and algorithmic “governance” play out in authentic learning contexts. In conclusion, the paper argues that theoretical and methodological principles of ANT grant for non-deterministic narrative of the heterogeneous nature of educational practice and have the potential to open the black-box of machine learning in the emerging networked learning settings of K-12 classrooms.

Agency and signification in learning with digital technologies

This paper put an approximation of Actor-Network Theory – ANT (cf. Callon & Latour, 1981; Latour, 1988; Latour, 1993; Latour, 1994) and representational philosophies deriving from the social semiotic multimodal theories (e.g., Hodge & Kress, 1988; Kress, 2010; Kress & van Leeuwen, 2021; van Leeuwen, 2005) to the fore to conceptualize how meaning-making (known as sign-making, learning, the process of signification, Bateman, 2018; Bezemer & Kress, 2016; Kress, 2010) via technologies come about from the technologies' various prompts. It is essential to recognize how representations such as semiotic resources – here, technologies and sign systems – have agency to form social practices. They are agentively selected, interpreted, and acted upon by the user into meaning-making activities (Jewitt, 2008, 2009, 2014). The technologies' front- and back-end properties' semiotic regimes (van Leeuwen, 2005; Djonov & van Leeuwen, 2018a) in different configurations can function as actants by symmetrically translating interests between humans and non-humans, into hybrid existences (Callon & Latour, 1981; Latour, 1994). Humans and technical objects are not rigid and independent substances (Platonic) but beings in constant (re)associations, which modify their existence (Callon & Latour, 1981; Latour, 1994). In that sense, Callon and Latour's claims can be understood in line with the genesis and development of representations that, from a historical epistemological perspective (Wartofsky, 1979), are in constant (re)associations by technologies, cultures, social practices, and humans. As humans mediate by means of their representations (Wartofksy, 1979), the representations are re-shaping and re-shaped through the history of reproduction that impacts interaction, meditation, and meaning-making (Kress, 2010; van Leeuwen, 2005; Wartofsky, 1979). The purpose of this paper is to briefly sketch a future research aspiration striving to theoretically approximate the ANT and representational philosophies and examine what kind of agency digital technologies impose on the users and how the users draw upon that imposition in their meaning-making. Crucially, such a reflection can heighten current understandings of the intricate relationships and networks created by humans and digital technologies in contemporary learning settings such as school to better appreciate students' digital learning from a representational agency perspective integrating the “signifieds-in-transformation” and “actants.” In preparation for future research studies, the following research question guides the theoretical explorations: who acts in the process of signification in learning activities with digital technologies?

Policy responses to the rise of global OTT platforms in Korea

Abstract The outbreak of COVID-19 and subsequent social distancing measures sparked a surge in demand and unprecedented growth opportunities for OTT (over-the-top) services. Despite the need to foster Korea’s domestic media and cultural industry, the Korean government has almost failed to properly operate OTT policies and the opportunity went to global operators. Although Korea’s content competitiveness has advanced to a global level, local OTTs continue to lag Netflix. This is not a concern confined to Korea. This paper identifies the focal actors of Korea’s local OTT platforms, analyzes the developmental phases of the Korean OTT market, and draws policy-related and industrial implications by ascertaining, which factors hinder local OTT services from achieving their desired development, within the context of the actor-network theory. This paper offers a fresh perspective on the policy responses to the rise of global OTT platforms and the downturn of local OTT platforms in Korea.

Geological adaptive intelligent control of earth pressure balance shield machine based on deep reinforcement learning

Scientific and precise control of tunnelling parameters is of utmost importance during the construction of shield machines. Given the complexity of the working environment, manual operation is highly prone to causing safety accidents. Therefore, achieving intelligent control of the shield machine is crucial. Based on this, this paper proposes a geological adaptive intelligent control method of earth pressure balance shield machine using the Deep Deterministic Policy Gradient (DDPG) algorithm as the framework, with Actor-Critic as the basis. Firstly, DDPG agent is constructed to replace the screw conveyor control system as the main body of strategy implementation. Secondly, an environmental model is established by utilizing the mechanism model between the sealed cabin pressure and the screw conveyor speed. The real-time sealed cabin pressure, target pressure, and pressure error serve as the state space, while the screw conveyor speed is used as the action space. A combined reward function is set based on safety and accuracy. Finally, the Actor network interacts with the environment under the supervision of the reward function and Critic network. Successful training is achieved when the cumulative reward value is maximized, resulting in the output of optimal control strategy. In this paper, the method dynamically regulates the screw conveyor speed by interacting with the geological environment, to realize the precise control of the sealed cabin pressure and ensure the dynamic balance between sealed cabin pressure and excavation face pressure. The test results show that this method has a good control effect on the sealed cabin pressure under various geological conditions, and can complete 72 kinds of soil transition tasks. It has strong soil adaptability and can respond well to the dynamic changes of soil conditions. This approach enhances the intelligence of the shield machine, mitigating inaccuracies attributed to human operation, which provides a guarantee of safe shield machine operation, whilst exhibiting valuable engineering applications.

Research on the Operation Mechanism of Science Popularization Bases Based on Actor-Network Theory

Government-led, multi-stakeholder participation in social mobilization mechanisms and market-oriented operational models are important characteristics of the development of science popularization in the new era. Based on Actor-Network Theory, this paper employs methods such as questionnaires and structured interviews to analyze and sort out the relationships between various actors through the four steps of problem definition, interest allocation, recruitment, and mobilization in the translation process. The study examines the role of the Science and Technology Association (STA) as the core actor in the operational process, identifies the interests and obstacles of various actors, and proposes suggestions to better improve the operational mechanism of science popularization bases from aspects such as strengthening policy guidance, establishing alliances of science popularization base interests, and enriching the forms of activities at science popularization bases.

Mutual capabilities: digital platforms in unpredictable pedagogical encounters

ABSTRACT This paper examines the implications of platforms as a repertoire for knowing and relating the intensities of pandemic restrictions on teaching and schoolwork. Building on platformisation in education, what work platforms do in unpredictable everyday pedagogical encounters is investigated. Specifically, the paper explores a methodological potential with platforms’ capabilities to pull some things together while supressing others. Drawing on ethnographic interviews with teachers and students in Sweden in 2021, everyday platform practices such as handling assignments, requests to connect online, and repetitive notifications are analysed with actor-network theory. Tensions of discomforts, resistance, and trust unfold critical acknowledgements of digital platforms as more complex objects than shaping pedagogical encounters prior to their practices. Instead, capabilities emerge as mutually rendered. The analysis shows that platforming well-bounded domains for clearer and more flexible teaching and schoolwork incoherently make educational practices less so, highlighting crucial openings to surprise and curiosity of pedagogical encounters.

Contested urban assemblages: ruination and restoration of postcolonial sites of memory

ABSTRACT Recent work on ruination has explored the capacity of ruins to embody multiple and contradictory meanings within a single site. This paper considers the capacity of ruined sites of memory to disrupt the relationship between past and present, celebration and shame, Self and Other. Ruins produce new expressions of time and space that transcend the intentions of either architect or spectator. Restoration, however, may attempt to overwrite this ambivalence with a single, dominant narrative. As a case study, this paper turns to the garden of tropical agronomy, a palimpsestic ruin in the Parisian suburbs that gives material form to both the imperial past and the process of forgetting the empire. It traces the history of the garden, and various unsuccessful attempts to restore it, before considering the successful renovation of the Tunisian Pavilion in 2020. Drawing from Actor-Network Theory, it analyses these interventions as acts of translation that alternately attempt to impose a singular meaning on the garden and enrich to its multi-layered form. Despite the intentions of human actants, throughout its recent history the garden’s uneasy multivocality has endured. Amidst conflicting and overlapping narratives, the garden haunts post-imperial Paris and undermines its attempts to consign empire to the past.

The Dynamic Evolution of College or University Roles in Aesthetic Education Assistance Projects in China: From Implementers to Co-Creators

The role of colleges and universities as transmitters of knowledge and active participants in aesthetic education assistance projects is pivotal. This study aims to investigate how the roles of these institutions in aesthetic education assistance projects have evolved in China. Through interviews with 10 college educators, students who endorse the project, and local teachers engaged in such initiatives, the research seeks to trace the dynamic shifts in the involvement of higher education institutions in fostering aesthetic education. The findings reveal that colleges and universities have transitioned from being mere implementers to assuming four key roles: resource providers, talent developers, knowledge advocates, and leaders in model practices within aesthetic education. A significant innovation of this research is the application of actor-network theory, which illustrates how these institutions have shifted from implementers to collaborators, creating a flexible network that enhances stakeholder engagement, attracts more participants, and encourages sustained active involvement. Ultimately, this study offers fresh insights into improving the initiative and co-innovation capacities of educational institutions and their networks, providing valuable experiences for future research and practical applications of aesthetic education assistance in developing areas to further aesthetic education's growth.

A Multi-Agent Reinforcement Learning-Based Task-Offloading Strategy in a Blockchain-Enabled Edge Computing Network

In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor–critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme’s comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively.

Can Archaeology be a Form of Colonialism? Exploring the Implications of the Actor-Network Theory

Can Archaeology be a Form of Colonialism? Exploring the Implications of the Actor-Network Theory

Global Perspective on EMR and eHealth

A goal of this exploratory study was to uncover the attitudes of the public towards health information technology (HIT) during a health crisis (COVID-19 pandemic). A socio-technical network formed by EMR/eHealth, COVID-19, and the tweeters was investigated using the Actor-Network-Theory (ANT) to understand global perspectives on healthcare technology, taking into account the human dimension and social reality in technology. Social Media (SM) analytics was used to mine HIT-related tweets from the early phases of the COVID-19 pandemic. Results show that positivity, trust, and anticipation are the three most significant emotions associated with electronic medical records (EMR) and eHealth from the tweets. Our analysis found an acceptance of HIT by the global public and optimism for its role in information dissemination, control and containment, and public health improvement. The ANT analysis reveals a virtual coalition building that occurred through discussions among notable network actors, and the role of crowdsourcing in disseminating digital health information.

Actor perceptions and network characteristics around climate-wise housing and construction in Finland

ABSTRACT Interaction between actors in sustainability transition is fundamental for generating knowledge about what constitutes a just, equitable and sustainable society. This paper focuses on the Finnish housing and construction sector as a socio-technical system, which currently accounts for about 40% of energy consumption and 35% of greenhouse gas emissions. A qualitative interview study with 18 organizations and a larger network study involving 35 actors were conducted to address the research questions: How do actors constitute a network and define climate-wise housing and construction? What specific focus areas can be identified around climate action? What issues support or inhibit climate-wise housing and construction? Although the network appeared relatively dense and inclusive, misalignment emerged upon closer examination. The actors were uniformly engaged in energy-related topics, but differed in their emphasis level on household choices, low-carbon materials, and the circular economy. Supporting and inhibiting factors were identified within three broad categories: alignment of goals, network characteristics, and transformation propensity. The findings suggest that climate action is gaining ground in housing and construction, but there is evidence of institutional inertia, a demand for accelerating support for intermediation, and a need to harness organizational resources and individual capabilities to create sustainability transitions.

ChatGPT as Co-Author? AI and Research Ethics

Should ChatGPT be viewed merely as a supportive tool for writers, or does it qualify as a co-author? As ChatGPT and similar language models are likely to become more prevalent in assisting with academic writing and research, it seems that we will face with two possibilities: an increase in ghostwriting that could finally undermine the integrity of the knowledge system, or the need to theoretical preparation to recognize the role of non-human contributors. Drawing on Actor-Network Theory, this article examines the question of whether this Chatbot meets, in principle, the requirements for co-authorship. Answering this question in affirmative, it delves into philosophical discussions concerning the agency, moral agency, and moral accountability of such technological entities.

Budget Refocusing And Reallocation Strategies: Rescuing Indonesia's Tourism Sector Amid The COVID-19 Pandemic

The COVID-19 pandemic has forced the Indonesian Government to refocus and reallocate the budget to overcome the economic and social impacts that have arisen. The tourism sector is one of the most affected sectors, so it requires special attention in budget allocation policies. This research aims to analyze the phenomenon of budget refocusing and reallocation in the tourism sector and the challenges faced in its implementation. Using a qualitative approach with a case study method, this research collected data from in-depth interviews with key stakeholders in the tourism sector, as well as documentation from various secondary sources. Data analysis was carried out using the interactive analysis model from Miles and Huberman, which consists of three main components: data display, data reduction, and conclusion drawing/verification. The research results show that the refocusing and reallocation of budgets implemented by regional governments, especially in the Special Region of Yogyakarta (DIY), has had a significant impact in supporting the tourism sector. However, challenges remain, including the need for innovation according to current tourism trends, hygienic tourism infrastructure and facilities, as well as improving tourism information and promotion networks. By using the Resource Based View (RBV) and Actor-Network Theory (ANT) approaches, this research provides insight into how resources and interactions between actors can be utilized to achieve competitive advantage and maintain sustainable performance in the tourism sector. This research also provides policy recommendations that can be implemented to support the recovery and sustainability of the tourism sector in the future.

Towards a platformised welfare state? How public administration, personal data, and third-sector welfare get entangled in a nation-wide digitalisation project

In the era of digital governance, the concept of Government as a Platform (GaaP) has gained significant attention. This article studies the implementation of this concept, taking the German Social Service Platform’ as a case study. The catalyst for the development of this public platform has been the German Online Access Act (Online-Zugangs-Gesetz or OZG), which marks a pivotal moment in the country’s government digitalization efforts. Employing the analytical toolkit of the Actor-Network-Theory (ANT), the article studies the sociotechnical processes shaping the evolution of this platform. It provides a thick description of the dynamics of translating generic ideas into an already existing landscape of institutions and technologies and explores how, through the actors’ interpretations, their negotiations, and their interactions with technologies, both ideas of traditional welfare governance (subsidiarity, welfare pluralism, and executive federalism) and ideas related to the platform concept (agility, user-centered design, once-only) are continuously redefined throughout the process.

Sex education against the algorithm: the algorithmically enforced deplatforming of YouTube sex edutainment

ABSTRACT Deplatforming of sexual content has increased across social media, usually operationalized by commercially charged, and algorithmically enforced, platform policies. This paper extends work on the algorithmic deplatforming of sex through the case study of how sex education content, or ‘sex edutainment’ on YouTube, is impacted by the platform’s algorithmic structures. Enrolling actor-network theory, we demonstrate the delegation work YouTube enacts through algorithms by presenting empirical findings from a multi-method study examining the assemblage of YouTube, sex edutainment influencers, and young people. The findings highlight that despite YouTube’s curated platform imaginary as an amplifier of voices, algorithmic delegation of platform governance creates significant barriers for influencers creating sex edutainment on YouTube. Although not contravening YouTube’s policies, influencers regularly battle demonetization, age-restrictions and algorithmic bias. This undermines the benefits of sex edutainment by limiting access to content, creates precarious financial environments for influencers and risks erosion of audience trust. Meanwhile, algorithms designed to protect users enact this governance without adequately protecting influencers themselves, predominantly women and LGBTQ+ individuals, from harm. Despite YouTube’s veneer of democratization, the social discourses and protectionist narratives that have destabilized traditional sex education efforts permeate our digital environments and can be seen in the algorithmic enactment of YouTube’s policies.

Traces of anti-programs against COVID-19: retailers' responses aimed at mitigating the spread of the virus

PurposeThe aim of the paper is to demonstrate how programs of action and anti-programs, concepts developed by Bruno Latour, are of excellent value in interpreting current world developments through a study of the effects and responses to the COVID-19 pandemic.Design/methodology/approachThe study was inspired by actor-network theory (ANT) and Bruno Latour's inclusion of nonhuman actors. In this case, I have studied how signs and other artifacts leave traces of anti-programs against the COVID-19 pandemic. Observations, in physical stores and online, are presented as the main empirical material used to identify traces of five anti-programs.FindingsThe five types of anti-programs identified were, namely (1) fighting the spread of the COVID-19 pandemic through prompts, (2) verbalizing responsibility, (3) creating a feeling of collectivity, (4) aspiring to heroism and (5) mobilizing support for continued business. The anti-programs were organized via a connection between human and nonhuman actors.Originality/valueThe study illustrates the usefulness of Latour's terminology in exploring contemporary sequences of events by means of using programs of action and anti-programs to study the case of retailers' responses to the COVID-19 pandemic. The study echoes ANT and Latour's ideas about including nonhuman actors in social studies. Moreover, the study demonstrates how these concepts can be productively introduced into studies of complex phenomena, by discussing the choice of viewpoint, how actors can be conjoined into one entity, the inclusion of nonobservable actors and the co-existence of an actor in both the program of action and the anti-programs.

A proposal for a balanced scorecard for the water utilities sector to address the United Nations sustainable development goals

PurposeThe purpose of this paper is to examine the technical, social and moral aspects of accounting through the implementation of a novel balanced scorecard (BSC) that addresses the United Nations Sustainable Development Goal (UN SDG) 6 – Clean Water and Sanitation – within the Portuguese water utilities sector.Design/methodology/approachA novel research design is adopted, using actor network theory (ANT) as a broad approach to frame the study. ANT emphasizes the importance of ever-evolving networks of relationships and how concepts such as the BSC are just as important in structuring social practice. A set of expert interviews was conducted with stakeholders in the water utilities sector in Portugal, which led to the iterative development of a context-relevant BSC proposal and associated indicators.FindingsA novel BSC architecture to achieve UN SDG 6 is proposed through a unique engagement between professionals and academics. The BSC, and the specific definition of indicators for an entire sector (water), contribute to bridging business processes with the common good to improve life and planetary conditions. Ultimately, the study discusses how the technical aspects of accounting can be enhanced to achieve social and moral imperatives. The paper also reflects on the limitations of broadening existing technical practices.Originality/valueThere is a burgeoning literature on how organizations are engaging with the UN SDG agenda. However, there is a dearth of studies on how management control systems are currently addressing, or can potentially contribute to measuring and managing specific UN SDGs such as Clean Water and Sanitation. This study makes a unique contribution to the literature by developing a novel BSC solution to SDG 6 measurement and management using a novel practitioner-led approach. Ultimately, our study highlights how accounting can be broadened to enhance technical practices while also serving a moral and social purpose.

Environmental Service Learning as University-Community Partnership: Using Actor-Network Theory to Examine a New Model of Engagement

Service learning has many documented benefits for students. The benefits to the communities are less clear. This study examines the unfolding of an environmental service-learning partnership from the perspective of one participating community liaison. We examine a new model of university-community engagement, where undergraduate students are paired with a local community to address environmental issues in courses that focus on adaptation and mitigation. We use actor-network theory (ANT) to explore the experiences of one community liaison, focusing specifically on factors that helped build and maintain the partnership and produced benefits for the community. Findings highlight the community liaison’s agency in negotiating partnership goals and determining the definition and treatment of environmental concerns. We conclude by identifying the insights an ANT perspective holds for service-learning and community-engagement research and practice.

Multi-objective explainable smart dispatch for integrated energy system based on an explainable MO-RL method

With the rapid development of renewable energy penetration and the transition to high-efficiency, low-carbon-footprint energy systems, renewable-based heat-electricity integrated energy systems (RHE-IES) have become a viable method. The dispatch and operation scheduling of the RHE-IES is more complex than before because a well-operated RHE-IES should be economically favorable, environmentally friendly, and feasible with respect to security constraints. However, it is not easy to balance these trade-offs with uncertainties. In this work, a multi-objective hierarchical deep reinforcement learning (H-DRL) method is proposed to address the dispatch of RHE-IES, and it is also explainable due to the design of the algorithmic structure, the decoupling of rewards, and the analysis of feature importance. First, the dispatch and operation scheduling problem of the RHE-IES is formulated considering economic, environmental, and security constraints and is converted to a Markov decision process. Second, a novel H-DRL method is proposed with a multi-critic, single-actor structure, where each objective is handled by a designated deep critic network with decoupled rewards, and the action value functions of different objectives are hierarchically optimized by the actor network. H-DRL can be trained in a comprehensive environment offline and provide fast online decisions considering the uncertainties of renewable generation. Third, a shapley additive explanations method is carried out utilizing the explainable nature of the H-DRL structure and the loss function design. The simulation results and case studies show the effectiveness of the proposed method in terms of balancing the tradeoffs of different objectives, overcoming uncertainties and enhancing AI interpretability.