Causal relationships play a pivotal role in research within the field of public administration. Ensuring reliable causal inference requires validating the predictability of these relationships, which is a crucial precondition. However, prediction has not garnered adequate attention within the realm of quantitative research in public administration and the broader social sciences. The advent of interpretable machine learning presents a significant opportunity to integrate prediction into quantitative research conducted in public administration. This article delves into the fundamental principles of interpretable machine learning while also examining its current applications in social science research. Building upon this foundation, the article further expounds upon the implementation process of interpretable machine learning, encompassing key aspects such as dataset construction, model training, model evaluation, and model interpretation. Lastly, the article explores the disciplinary value of interpretable machine learning within the field of public administration, highlighting its potential to enhance the generalization of inference, facilitate the selection of optimal explanations for phenomena, stimulate the construction of theoretical hypotheses, and provide a platform for the translation of knowledge. As a complement to traditional causal inference methods, interpretable machine learning ushers in a new era of credibility in quantitative research within the realm of public administration.
Susana Lopez-Moreno, Eric Dolores-Cuenca, Sangil Kim
Reproducibility remains a challenge in machine learning research. While code and data availability requirements have become increasingly common, post-publication verification in journals is still limited and unformalized. This position paper argues that it is plausible for journals and conference proceedings to implement post-publication verification. We propose a modification to ACM pre-publication verification badges that allows independent researchers to submit post-publication code replications to the journal, leading to visible verification badges included in the article metadata. Each article may earn up to two badges, each linked to verified code in its corresponding public repository. We describe the motivation, related initiatives, a formal framework, the potential impact, possible limitations, and alternative views.
Public space quality assessment lacks systematic methodologies that integrate factors across diverse spatial typologies while maintaining context-specific relevance. Current approaches remain fragmented within disciplinary boundaries, limiting comprehensive evaluation and comparative analysis across different space types. This study develops a systematic, data-driven framework for assessing public space quality through the algorithmic integration of empirical research findings. Using a 7-phase methodology, we transform 1,207 quality factors extracted from 157 peer-reviewed studies into a validated hierarchical taxonomy spanning six public space typologies: urban spaces, open spaces, green spaces, parks and waterfronts, streets and squares, and public facilities. The methodology combines semantic analysis, cross-typology distribution analysis, and domain knowledge integration to address terminological variations and functional relationships across space types. The resulting framework organizes 1,029 unique quality factors across 14 main categories and 66 subcategories, identifying 278 universal factors applicable across all space types, 397 space-specific factors unique to particular typologies, and 124 cross-cutting factors serving multiple functions. Framework validation demonstrates systematic consistency in factor organization and theoretical alignment with established research on public spaces. This research provides a systematic methodology for transforming empirical public space research into practical assessment frameworks, supporting evidence-based policy development, design quality evaluation, and comparative analysis across diverse urban contexts.
Guillermo Baltra, Tarang Saluja, Yuri Pradkin
et al.
The Internet provides global connectivity by virtue of a public core -- the routable public IP addresses that host services and to which cloud, enterprise, and home networks connect. Today the public core faces many challenges to uniform, global reachability: firewalls and access control lists, commercial disputes that stretch for days or years, and government-mandated sanctions. We define two algorithms to detect partial connectivity: Taitao detects peninsulas of persistent, partial connectivity, and Chiloe detects islands, when one or more computers are partitioned from the public core. These new algorithms apply to existing data collected by multiple long-lived measurement studies. We evaluate these algorithms with rigorous measurements from two platforms: Trinocular, where 6 locations observe 5M networks frequently, RIPE Atlas, where 10k locations scan the DNS root frequently, and validate adding a third: CAIDA Ark, where 171 locations traceroute to millions of networks daily. Root causes suggest that most peninsula events (45%) are routing transients, but most peninsula-time (90%) is due to long-lived events (7%). We show that the concept of peninsulas and islands can improve existing measurement systems. They identify measurement error and persistent problems in RIPE's DNSmon that are $5\times$ to $9.7\times$ larger than the operationally important changes of interest. They explain previously contradictory results in several outage detection systems. Peninsulas are at least as common as Internet outages, posing new research direction.
This study focused on the development of science and technology can give these demands make college need the model of public relations to develop the college the purpose of research is 1) Press agentry / publicity model is a model where information moves in one direction, from organization to public. This model is the oldest form of public relations and this model has the same meaning as promotion and publicity. Public relations practitioners who practice this model are always looking for opportunities to get their organization's good name to appear in the media.2) Public Information Model. This model is different from press agentry, because the main purpose is to notify the public and not for promotion and publicity, but the communication path remains one way.3) Model Two-way Asymmetric Model. This model views public relations as a work of scientific persuasion. This model applies social science research methods to improve the effectiveness of persuasion of the messages conveyed. Public relations practitioners with this model use surveys, interviews, and focus groups to measure and assess the public so that they can design public relations programs that can gain support from key public.4) Two-way symmetric model. This model describes a public relations orientation in which organizations and the public adapt to each other. This model focuses on the use of social science research methods to gain a sense of mutual understanding and two-way communication between the public and the organization rather than one-way persuasion.Of the four models, which should be developed by Islamic educational institutions is a two way symmetric model. This is because this model holds a reciprocal relationship between Islamic educational institutions and the community.so, the model of public relations model in educational institutions of the college is very important.
. The research aims to determine whether companies using CSR in their marketing communication believe this approach has a positive effect on CPV and what forms of communication mix they most often use for this purpose. To achieve this goal, the method of online questionnaire survey was selected for data collection. The methods of correlation and regression analysis and structural equation modelling were used for data processing. Results obtained by interviewing business representatives show that companies often need to realize these are CSR principles to implement CSR activities. The most commonly used CSR communication tools include "Public relations & publicity" and "Internet marketing"; however, no statistically important correlation between these tools has been confirmed. The research results confirm that companies using CSR for marketing communication are aware of its importance concerning the brand's positive perception; on the other hand, from the perspective of the interviewed companies, no link between CSR, its communication
Tsamrotul Fuadah, Rully Khairul, Encang Anwar
et al.
Perpustakaan Kementerian Kelautan dan Perikanan need to carry out promotions to introduce themselves to their target users. The promotions of libraries through Instagram media is interesting to talk because it is one of the core activities that libraries carry out using media that is widely used by many people. This study uses a qualitative descriptive method to analyze and describe library promotion in detail with data collection techniques are observation, interviews, and literature studies. Data analysis techniques that used in this study are data reduction, data display, and conclusion drawing/verification. Perpustakaan Kementerian Kelautan dan Perikanan has implemented elements of the promotion mix are sales promotion by procuring quizzes with prizes and awarding the best library users, personal selling through giving responses in comments and Instagram DMs, public relations and publicity through holding public relations activities and publishing activities, and direct marketing through the dissemination of information directly through Instagram. Advertising is not carried out in library promotion. Keywords: promotions of libraries; promotio mix; Perpustakaan KKP
AbstractNew Zealand provides a unique comparative case with its well‐embedded, comprehensive and flexible public dispute resolution services. Changes from collective to individual disputes and a resulting rise in institutional caseload have occurred since 1990, culminating in increased public information, enforcement and dispute resolution efforts. However, debates exist about improving access to justice, reducing legalism and providing proactive conflict resolution.
Robin Haunschild, Lutz Bornmann, Devendra Potnis
et al.
One way to assess a certain aspect of the value of scientific research is to measure the attention it receives on social media. While previous research has mostly focused on the "number of mentions" of scientific research on social media, the current study applies "topic networks" to measure public attention to scientific research on Twitter. Topic networks are the networks of co-occurring author keywords in scholarly publications and networks of co-occurring hashtags in the tweets mentioning those scholarly publications. This study investigates which topics in opioid scholarly publications have received public attention on Twitter. Additionally, it investigates whether the topic networks generated from the publications tweeted by all accounts (bot and non-bot accounts) differ from those generated by non-bot accounts. Our analysis is based on a set of opioid scholarly publications from 2011 to 2019 and the tweets associated with them. We use co-occurrence network analysis to generate topic networks. Results indicated that Twitter users have mostly used generic terms to discuss opioid publications, such as "Opioid," "Pain," "Addiction," "Treatment," "Analgesics," "Abuse," "Overdose," and "Disorders." Results confirm that topic networks provide a legitimate method to visualize public discussions of health-related scholarly publications and how Twitter users discuss health-related scientific research differently from the scientific community. There was a substantial overlap between the topic networks based on the tweets by all accounts and non-bot accounts. This result indicates that it might not be necessary to exclude bot accounts for generating topic networks as they have a negligible impact on the results.
Energy storage has exhibited great potential in providing flexibility in power system to meet critical peak demand and thus reduce the overall generation cost, which in turn stabilizes the electricity prices. In this work, we exploit the opportunities for the independent system operator (ISO) to invest and manage storage as public asset, which could systematically provide benefits to the public. Assuming a quadratic generation cost structure, we apply parametric analysis to investigate the ISO's problem of economic dispatch, given variant quantities of storage investment. This investment is beneficial to users on expectation. However, it may not necessarily benefit everyone. We adopt the notion of marginal system cost impact (MCI) to measure each user's welfare and show its relationship with the conventional locational marginal price. We find interesting convergent characteristics for MCI. Furthermore, we perform $k$-means clustering to classify users for effective user profiling and conduct numerical studies on both prototype and IEEE test systems to verify our theoretical conclusions.
Do mass media influence people's opinion of other countries? Using BERT, a deep neural network-based natural language processing model, we analyze a large corpus of 267,907 China-related articles published by The New York Times since 1970. We then compare our output from The New York Times to a longitudinal data set constructed from 101 cross-sectional surveys of the American public's views on China. We find that the reporting of The New York Times on China in one year explains 54% of the variance in American public opinion on China in the next. Our result confirms hypothesized links between media and public opinion and helps shed light on how mass media can influence public opinion of foreign countries.
Simone Grimaldi, Aamir Mahmood, Syed Ali Hassan
et al.
The limited coexistence capabilities of current Internet-of-things (IoT) wireless standards produce inefficient spectrum utilization and mutual performance impairment. The entity of the issue escalates in industrial IoT (IIoT) applications, which instead have stringent quality-of-service requirements and exhibit very-low error tolerance. The constant growth of wireless applications over unlicensed bands mandates then the adoption of dynamic spectrum access techniques, which can greatly benefit from interference mapping over multiple dimensions of the radio space. In this article, the authors analyze the critical role of real-time interference detection and classification mechanisms that rely on IIoT devices only, without the added complexity of specialized hardware. The trade-offs between classification performance and feasibility are analyzed in connection with the implementation on low-complexity IIoT devices. Moreover, the authors explain how to use such mechanisms for enabling IIoT networks to construct and maintain multidimensional interference maps at run-time in an autonomous fashion. Lastly, the authors give an overview of the opportunities and challenges of using interference maps to enhance the performance of IIoT networks under interference.
Shihan Wang, Marijn Schraagen, Erik Tjong Kim Sang
et al.
Public sentiment (the opinions, attitudes or feelings expressed by the public) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. While the 'staying-at-home' policy makes face-to-face interactions and interviews challenging, analysing real-time Twitter data that reflects public opinion toward policy measures is a cost-effective way to access public sentiment. In this context, we collect streaming data using the Twitter API starting from the COVID-19 outbreak in the Netherlands in February 2020, and track Dutch general public reactions on governmental measures and announcements. We provide temporal analysis of tweet frequency and public sentiment over the past seven months. We also identify public attitudes towards two Dutch policies in case studies: one regarding social distancing and one regarding wearing face masks. By presenting those preliminary results, we aim to provide visibility into the social media discussions around COVID-19 to the general public, scientists and policy makers. The data collection and analysis will be updated and expanded over time.
Eduardo J. Aguilar, Valmir C. Barbosa, Raul Donangelo
et al.
Bacterial quorum sensing is the communication that takes place between bacteria as they secrete certain molecules into the intercellular medium that later get absorbed by the secreting cells themselves and by others. Depending on cell density, this uptake has the potential to alter gene expression and thereby affect global properties of the community. We consider the case of multiple bacterial species coexisting, referring to each one of them as a genotype and adopting the usual denomination of the molecules they collectively secrete as public goods. A crucial problem in this setting is characterizing the coevolution of genotypes as some of them secrete public goods (and pay the associated metabolic costs) while others do not but may nevertheless benefit from the available public goods. We introduce a network model to describe genotype interaction and evolution when genotype fitness depends on the production and uptake of public goods. The model comprises a random graph to summarize the possible evolutionary pathways the genotypes may take as they interact genetically with one another, and a system of coupled differential equations to characterize the behavior of genotype abundance in time. We study some simple variations of the model analytically and more complex variations computationally. Our results point to a simple trade-off affecting the long-term survival of those genotypes that do produce public goods. This trade-off involves, on the producer side, the impact of producing and that of absorbing the public good. On the non-producer side, it involves the impact of absorbing the public good as well, now compounded by the molecular compatibility between the producer and the non-producer. Depending on how these factors turn out, producers may or may not survive.
This chapter demonstrates how the discourse of blight shaped renewal and how the racialization of urban space underpinned housing markets and urban renewal. It talks about Chicago's political and business leaders who worked to turn some of Chicago's blighted land into productive industrial space. It also identifies agency officials who believed that the overhaul of some of Chicago's “waste lands” for industrial redevelopment would reverse decline by delivering jobs, taxes, and prosperity. The chapter describes the new set of industrial lands and the associated set of property relations that emerged out of urban renewal, which were created by all three levels of government and legitimized in the courts. It cites the Housing Act of 1937, which permitted land clearance and slum demolition for public housing and the Housing Act of 1949, which channeled federal funds to cities so that blighted districts could be redeveloped as predominantly residential.
This chapter analyzes the attempts by Chicago's public–private partnerships to fight industrial decline in the 1950s by linking blight, property, and redevelopment. It recounts the South Side Planning Board's failed attempt to redevelop an area of the South Side as an industrial district. It also looks at the fight between residents and the Bodine Electric Company over the property rights embedded in the zoning ordinance and the way in which these rights shaped industrial redevelopment. The chapter explores industrial property relations that were on the agenda of Chicago's place-dependent leaders by the 1940s. It describes the fortunes of Chicago's industry over the previous two decades, in which a growing number of people were looking to find ways to combat manufacturing decline, industrial blight, and a dwindling tax base.
Akira Inokuchi, Yusuf Sulistyo Nugroho, Supatsara Wattanakriengkrai
et al.
Academic publications have been evaluated in terms of their impact on research communities based on many metrics, such as the number of citations. On the other hand, the impact of academic publications on industry has been rarely studied. This paper investigates how academic publications contribute to software development by analyzing publication citations in source code comments in open source software repositories. We propose an automated approach for detecting academic publications based on Named Entity Recognition, and achieve 0.90 in $F_1$ as detection accuracy. We conduct a large-scale study of publication citations with 319,438,977 comments collected from 25,925 active repositories written in seven programming languages. Our findings indicate that academic publications can be knowledge sources for software development. These referenced publications are particularly from journals. In terms of knowledge transfer, algorithm is the most prevalent type of knowledge transferred from the publications, with proposed formulas or equations typically implemented in methods or functions in source code files. In a closer look at GitHub repositories referencing academic publications, we find that science-related repositories are the most frequent among GitHub repositories with publication citations, and that the vast majority of these publications are referenced by repository owners who are different from the publication authors. We also find that referencing older publications can lead to potential issues related to obsolete knowledge.
We plan to simulate a private and unlinkable exchange of messages by using a Public bulletin board and Mix networks in Opportunistic networks. This Opportunistic network uses a secure and privacy-friendly asynchronous unidirectional message transmission protocol. By using this protocol, we create a Public bulletin board in a network that makes individuals send or receive events unlinkable to one another . With the design of a Public bulletin board in an Opportunistic network, the clients can use the benefits of this Public bulletin board in a safe environment. When this Opportunistic network uses the protocol, it can guarantee an unlinkable communication based on the Mix networks. The protocol can work with the Public bulletin board exclusively with acceptable performance. Also, this simulation can be used for hiding metadata in the bidirectional message exchange in some messengers such as WhatsApp. As we know, one of the main goals of a messenger like WhatsApp is to protect the social graph. By using this protocol, a messenger can protect social graph and a central Public bulletin board.
The purpose of this research is to reveal about (1) PR efforts, (2) creating a corporate image (3) the use of social media in building a corporate image. The results showed that the company's image would be better if PR in a company used social media as a tool to inform the company's excellence to the public. Because at this time the public tends to use social media at all levels of age, be it children, spiritual, adult to old age. technological developments allow people to communicate easily. A company must be able to keep abreast of market developments and demands because the movement of information and communication, internal or external knowledge and public awareness increases. Industrial competition requires everything to be communicated transparently. The problem might appear unexpectedly. Small problems can become big if they are not taken seriously. This demand makes the need for Public Relations must use social media in building the company's image. Public Relations emerged as a helper in a crisis and was responsible for developing the company's positive image. Positive images are not simply formed, but efforts are needed to build and maintain them. A positive reputation is a way to get a positive image. The success of Public Relations in gaining publicity can be obtained from harmonious relations with social media in this globalization era.