Ethnography attends to relations among people, practices, and the technologies that mediate them. Central to this method is the duality of roles ethnographers navigate as researchers and participants and as outsiders and insiders. However, the rise of digital platforms has introduced new opportunities as well as practical and ethical challenges that reshape these dualities across hybrid media environments spanning both online and offline contexts. Drawing on two case studies of VRChat and WhatsApp, we examine how ethnographers employ diverse tactics to study both enduring and emerging socio-cultural issues of race and caste, particularly those that form what are often called publics. We propose emergent relationality as a key analytic for understanding the mutual shaping of ethnographers, platforms, and publics. In this work, emergent relationality offers registers for analyzing how positionality and hybrid media environments constitute and condition what can be accessed, articulated, and made public.
Despite the recent interest in the deep state topic, the concept remains elusive. This research utilizes bibliometric metadata from 64 articles published between 2009 and 2024 to identify the intellectual structure and origins of the concept. The findings indicate that discussions on the deep state are fragmented across disciplines such as political science, public administration, law, international relations, and history, intersecting with themes like democratization, civilian‐military relations, and informal networks, yet the concept has not formed into a standalone idea. Interpretations of the concept differ regionally and temporally: in the Middle East, it is associated with civilian‐military dynamics; in Thailand, with interactions among the monarchy, judiciary, and officials; in the United States, historically, the focus has been the military‐industrial complex, which shifted to the administrative state post‐Trump. These variations highlight the need for a comprehensive theoretical framework, for which public administration could provide essential theoretical and methodological support.
This study focuses on the people inhabiting an internal frontier of global capital marked by the zone of a waste landfill and its surrounding industrial belt. While the external frontiers of capitalist accumulation are traceable to identifiable corporations, internal frontiers involve ambiguous work and organizational relations. We draw on fieldwork at a settlement near a waste landfill in Ahmedabad, India. We weave research on infrastructures with organizational studies of violence to examine the (re-) production of these internal frontiers. We show how the state and private actors inflict socio-economic ruination and govern through infrastructural violence – such as exclusions from public infrastructures, proliferating private infrastructures and exposure to toxic infrastructures – to produce the internal frontier. Residents endure life through the reparative infrastructural work of salvaging and patching infrastructures. We contribute to organizational research on violence by highlighting the under-theorized internal frontiers of global capital that comprise large swathes of the population. Furthermore, using infrastructure as an analytic lens, we open new terrains of inquiry into work and organizing in the capitalist mode of production. We show how reparative infrastructural work at the internal frontier transgresses Global North-centric formulations of work. We advance nascent organization studies on majoritarian political formations.
Alex S. Taylor, Noortje Marres, Mercedes Bunz
et al.
The street has emerged as a primary site where everyday publics are confronted with AI as an infrastructural phenomenon, as machine learning-based systems are now commonly deployed in this setting in the form of automated cars, facial recognition, smart billboards and the like. While these deployments of AI in the street have attracted significant media attention and public controversy in recent years, the presence of AI in the street often remains inscrutable, and many everyday publics are unaware of it. In this paper, we explore the challenges and possibilities of everyday public engagement with AI in the situated environment of city streets under these paradoxical conditions. Combining perspectives and approaches from social and cultural studies of AI, Design Research and Science and Technology Studies (STS), we explore the affordances of the street as a site for 'material participation' in AI through design-based interventions: the creation of 'everyday AI observatories.' We narrate and reflect on our participatory observations of AI in five city streets in the UK and Australia and highlight a set of tensions that emerged from them: 1) the framing of the street as a transactional environment, 2) the designed invisibility of AI and its publics in the street 3) the stratification of street environments through statistical governance. Based on this discussion and drawing on Jane Jacobs' notion of "eyes on the street," we put forward the relational notion of "reciprocity deficits" between AI infrastructures and everyday publics in the street. The conclusion reflects on the consequences of this form of social invisibility of AI for situated engagement with AI by everyday publics in the street and for public trust in urban governance.
Jonathan Bright, Florence E. Enock, Saba Esnaashari
et al.
Generative AI has the potential to transform how public services are delivered by enhancing productivity and reducing time spent on bureaucracy. Furthermore, unlike other types of artificial intelligence, it is a technology that has quickly become widely available for bottom-up adoption: essentially anyone can decide to make use of it in their day to day work. But to what extent is generative AI already in use in the public sector? Our survey of 938 public service professionals within the UK (covering education, health, social work and emergency services) seeks to answer this question. We find that use of generative AI systems is already widespread: 45% of respondents were aware of generative AI usage within their area of work, while 22% actively use a generative AI system. Public sector professionals were positive about both current use of the technology and its potential to enhance their efficiency and reduce bureaucratic workload in the future. For example, those working in the NHS thought that time spent on bureaucracy could drop from 50% to 30% if generative AI was properly exploited, an equivalent of one day per week (an enormous potential impact). Our survey also found a high amount of trust (61%) around generative AI outputs, and a low fear of replacement (16%). While respondents were optimistic overall, areas of concern included feeling like the UK is missing out on opportunities to use AI to improve public services (76%), and only a minority of respondents (32%) felt like there was clear guidance on generative AI usage in their workplaces. In other words, it is clear that generative AI is already transforming the public sector, but uptake is happening in a disorganised fashion without clear guidelines. The UK's public sector urgently needs to develop more systematic methods for taking advantage of the technology.
Based on the quarterly data from 26 advanced economies (AEs) and 18 emerging market economies (EMs) over the past two decades, this paper estimates the short- and medium-term impacts of financial cycles on the duration and amplitude of public debt cycles. The results indicate that public debt expansions are larger than their contractions in duration and amplitude, aligning with the "deficit bias hypothesis" and being more pronounced in EMs than in AEs. The impacts of various financial cycles are different. Specifically, credit cycles in EMs significantly impact the duration and amplitude of public debt cycles. Notably, short- and medium-term credit booms in EMs shorten the duration of public debt contractions and reduce the amplitude. Fast credit growth in AEs prolongs the duration of public debt expansions and increases the amplitude. However, credit cycles in AEs show no significant impact. For house price cycles, the overall impact is stronger in EMs than in AEs, differing between short- and medium-term cycles. Finally, the impact of equity price cycles is significant in the short term, but not in the medium term. Equity price busts are more likely to prolong the expansion of public debt in EMs while increasing the amplitude of public debt contractions in AEs. Uncovering the impacts of multiple financial cycles on public debt cycles provides implications for better debt policies under different financial conditions.
Renan Lima Baima, Iván Abellán Álvarez, Ivan Pavić
et al.
In response to the European Commission's aim of cutting carbon emissions by 2050, there is a growing need for cutting-edge solutions to promote low-carbon energy consumption in public infrastructures. This paper introduces a Proof of Concept (PoC) that integrates the transparency and immutability of blockchain and the Internet of Things (IoT) to enhance energy efficiency in tangible government-held public assets, focusing on curbing carbon emissions. Our system design utilizes a forecasting and optimization framework, inscribing the scheduled operations of heat pumps on a public sector blockchain. Registering usage metrics on the blockchain facilitates the verification of energy conservation, allows transparency in public energy consumption, and augments public awareness of energy usage patterns. The system fine-tunes the operations of electric heat pumps, prioritizing their use during low-carbon emission periods in power systems occurring during high renewable energy generations. Adaptive temperature configuration and schedules enable energy management in public venues, but blockchains' processing power and latency may represent bottlenecks setting scalability limits. However, the proof-of-concept weakness and other barriers are surpassed by the public sector blockchain advantages, leading to future research and tech innovations to fully exploit the synergies of blockchain and IoT in harnessing sustainable, low-carbon energy in the public domain.
Johan Linåker, Gregorio Robles, Deborah Bryant
et al.
The proliferation of Open Source Software (OSS) adoption and collaboration has surged within industry, resulting in its ubiquitous presence in commercial offerings and shared digital infrastructure. However, in the public sector, both awareness and adoption of OSS is still in its infancy due to a number of obstacles including regulatory, cultural, and capacity-related challenges. This special issue is a call for action, highlighting the necessity for both research and practice to narrow the gap, selectively transfer and adapt existing knowledge, as well as generate new knowledge to enable the public sector to fully harness the potential benefits OSS has to offer.
Lucas Potin, Vincent Labatut, Pierre-Henri Morand
et al.
Public Procurement refers to governments' purchasing activities of goods, services, and construction of public works. In the European Union (EU), it is an essential sector, corresponding to 15% of the GDP. EU public procurement generates large amounts of data, because award notices related to contracts exceeding a predefined threshold must be published on the TED (EU's official journal). Under the framework of the DeCoMaP project, which aims at leveraging such data in order to predict fraud in public procurement, we constitute the FOPPA (French Open Public Procurement Award notices) database. It contains the description of 1,380,965 lots obtained from the TED, covering the 2010--2020 period for France. We detect a number of substantial issues in these data, and propose a set of automated and semi-automated methods to solve them and produce a usable database. It can be leveraged to study public procurement in an academic setting, but also to facilitate the monitoring of public policies, and to improve the quality of the data offered to buyers and suppliers.
Celebrities can significantly influence the public towards any desired outcome. In a bid to tackle an infectious disease, a leader (government) exploits such influence towards motivating a fraction of public to get vaccinated, sufficient enough to ensure eradication. The leader also aims to minimize the vaccinated fraction of public (that ensures eradication) and use minimal incentives to motivate the influencers; it also controls vaccine-supply-rates. Towards this, we consider a three-layered Stackelberg game, with the leader at the top. A set of influencers at the middle layer are involved in a stochastic vaccination game driven by incentives. The public at the bottom layer is involved in an evolutionary game with respect to vaccine responses. We prove the disease can always be eradicated once the public is sufficiently sensitive towards the vaccination choices of the influencers -- with a minimal fraction of public vaccinated. This minimal fraction depends only on the disease characteristics and not on other aspects. Interestingly, there are many configurations to achieve eradication, each configuration is specified by a dynamic vaccine-supply-rate and a number -- this number represents the count of the influencers that needs to be vaccinated to achieve the desired influence. Incentive schemes are optimal when this number equals all or just one; the former curbs free-riding among influencers while the latter minimizes the dependency on influencers.
Emily Escamilla, Martin Klein, Talya Cooper
et al.
The definition of scholarly content has expanded to include the data and source code that contribute to a publication. While major archiving efforts to preserve conventional scholarly content, typically in PDFs (e.g., LOCKSS, CLOCKSS, Portico), are underway, no analogous effort has yet emerged to preserve the data and code referenced in those PDFs, particularly the scholarly code hosted online on Git Hosting Platforms (GHPs). Similarly, the Software Heritage Foundation is working to archive public source code, but there is value in archiving the issue threads, pull requests, and wikis that provide important context to the code while maintaining their original URLs. In current implementations, source code and its ephemera are not preserved, which presents a problem for scholarly projects where reproducibility matters. To understand and quantify the scope of this issue, we analyzed the use of GHP URIs in the arXiv and PMC corpora from January 2007 to December 2021. In total, there were 253,590 URIs to GitHub, SourceForge, Bitbucket, and GitLab repositories across the 2.66 million publications in the corpora. We found that GitHub, GitLab, SourceForge, and Bitbucket were collectively linked to 160 times in 2007 and 76,746 times in 2021. In 2021, one out of five publications in the arXiv corpus included a URI to GitHub. The complexity of GHPs like GitHub is not amenable to conventional Web archiving techniques. Therefore, the growing use of GHPs in scholarly publications points to an urgent and growing need for dedicated efforts to archive their holdings in order to preserve research code and its scholarly ephemera.
Public health is the most recent of the biomedical sciences to be seduced by the trendy moniker "precision." Advocates for "precision public health" (PPH) call for a data-driven, computational approach to public health, leveraging swaths of genomic "big data" to inform public health decision-making. Yet, like precision medicine, PPH oversells the value of genomic data to determine health outcomes, but on a population-level. A large historical literature has shown that over-emphasizing heredity tends to disproportionately harm underserved minorities and disadvantaged communities. By comparing and contrasting PPH with an earlier attempt at using big data and genetics, in the Progressive era (1890-1920), we highlight some potential risks of a genotype-driven preventive public health. We conclude by suggesting that such risks may be avoided by prioritizing data integration across many levels of analysis, from the molecular to the social.
There are many news articles reporting the obstacles confronting poverty-stricken households in access to public transits. These barriers create a great deal of inconveniences for these impoverished families and more importantly, they contribute a lot of social inequalities. A typical approach addressing the issue is to build more transport infrastructure to offer more opportunities to access the public transits especially for those deprived communities. Examples include adding more bus lines connecting needy residents to railways systems and extending existing bus lines to areas with low socioeconomic status. Recently, a new strategy is proposed, which is to harness the ubiquitous ride-hailing services to connect disadvantaged households with the nearest public transportations. Compared with the former infrastructure-based solution, the ride-hailing-based strategy enjoys a few exclusive benefits such as higher effectiveness and more flexibility. In this paper, we propose an optimization model to study how to integrate the two approaches together for equity-promotion purposes. Specifically, we aim to design a strategy of allocating a given limited budget to different candidate programs such that the overall social equity is maximized, which is defined as the minimum covering ratio among all pre-specified protected groups of households (based on race, income, etc.). We have designed a linear-programming (LP) based rounding algorithm, which proves to achieve an optimal approximation ratio of 1-1/e. Additionally, we test our algorithm against a few baselines on real data assembled by outsourcing multiple public datasets collected in the city of Chicago. Experimental results confirm our theoretical predictions and demonstrate the effectiveness of our LP-based strategy in promoting social equity, especially when the budget is insufficient.
In this paper, we revisit the problem of using in-distribution public data to improve the privacy/utility trade-offs for differentially private (DP) model training. (Here, public data refers to auxiliary data sets that have no privacy concerns.) We design a natural variant of DP mirror descent, where the DP gradients of the private/sensitive data act as the linear term, and the loss generated by the public data as the mirror map. We show that, for linear regression with feature vectors drawn from a non-isotropic sub-Gaussian distribution, our algorithm, PDA-DPMD (a variant of mirror descent), provides population risk guarantees that are asymptotically better than the best known guarantees under DP (without having access to public data), when the number of public data samples ($n_{\sf pub}$) is sufficiently large. We further show that our algorithm has natural "noise stability" properties that control the variance due to noise added to ensure DP. We demonstrate the efficacy of our algorithm by showing privacy/utility trade-offs on four benchmark datasets (StackOverflow, WikiText-2, CIFAR-10, and EMNIST). We show that our algorithm not only significantly improves over traditional DP-SGD, which does not have access to public data, but to our knowledge is the first to improve over DP-SGD on models that have been pre-trained with public data.
When can cooperation arise from self-interested decisions in public goods games? And how can we help agents to act cooperatively? We examine these classical questions in a pivotal participation game, a variant of public good games, where heterogeneous agents make binary participation decisions on contributing their endowments, and the public project succeeds when it has enough contributions. We prove it is NP-complete to decide the existence of a cooperative Nash equilibrium such that the project succeeds. We also identify two natural special scenarios where this decision problem is tractable. We then propose two algorithms to help cooperation in the game. Our first algorithm adds an external investment to the public project, and our second algorithm uses matching funds. We show that the cost to induce a cooperative Nash equilibrium is near-optimal for both algorithms. Finally, the cost of matching funds can always be smaller than the cost of adding an external investment. Intuitively, matching funds provide a greater incentive for cooperation than adding an external investment does.
Abstract Background Public relations—a marketing communications method involving the use of publicity and other unpaid promotional methods to deliver messages—historically has served as the communicative workhorse of the health services industry, representing the predominant pathway over many decades by which health and medical facilities conveyed stories to the public. While other components of the marketing communications mix, perhaps most notably that of advertising, have now captured a significant portion of interest, attention, and use by healthcare establishments, public relations remains a valuable communicative avenue when deployed properly. Discussion As an unpaid method of promotion, public relations is uniquely positioned among its counterparts in the marketing communications mix which require direct expenditures to reach audiences. Typically effected by preparing and submitting press releases to news media firms in hopes that they, in turn, will present given stories to their audiences, limitations are somewhat obvious as transmission control rests with external entities. But overcoming limitations is possible with prudent strategies. This article presents Willis-Knighton Health System’s associated strategies, along with a range of public relations insights from decades of deployment experience. Conclusions Prudently deployed and led by guiding strategies, public relations offers health and medical organizations opportunities to engage audiences in an efficient and highly credible manner. Courtesy of its unique properties, public relations capably can complement other marketing communications, operating synergistically to help healthcare institutions achieve their conveyance goals, fostering exchange and bolstering market share. Careful operationalization of this marketing communications avenue can help healthcare establishments realize their full communicative potential.
A shallow semantical embedding for public announcement logic with relativized common knowledge is presented. This embedding enables the first-time automation of this logic with off-the-shelf theorem provers for classical higher-order logic. It is demonstrated (i) how meta-theoretical studies can be automated this way, and (ii) how non-trivial reasoning in the target logic (public announcement logic), required e.g. to obtain a convincing encoding and automation of the wise men puzzle, can be realized. Key to the presented semantical embedding -- in contrast, e.g., to related work on the semantical embedding of normal modal logics -- is that evaluation domains are modeled explicitly and treated as additional parameter in the encodings of the constituents of the embedded target logic, while they were previously implicitly shared between meta logic and target logic.
Fatima N. AL-Aswadi, Huah Yong Chan, Keng Hoon Gan
With the large volume of unstructured data that increases constantly on the web, the motivation of representing the knowledge in this data in the machine-understandable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The current ontology repositories are quite limited either for their scope or for currentness. In addition, the current ontology extraction systems have many shortcomings and drawbacks, such as using a small dataset, depending on a large amount predefined patterns to extract semantic relations, and extracting a very few types of relations. The aim of this paper is to introduce a proposal of automatically extracting semantic concepts and relations from scientific publications. This paper suggests new types of semantic relations and points out of using deep learning (DL) models for semantic relation extraction.
We introduce a variant of quantum signatures in which nonbinary symbols are signed instead of bits. The public keys are fingerprinting states, just as in the scheme of Gottesman and Chuang, but we allow for multiple ways to reveal the private key partially. The effect of this modification is a reduction of the number of qubits expended per message bit. We give a security proof and we present numerical results that show how the improvement in public key size depends on the message length.