Hasil untuk "Public relations. Industrial publicity"

Brian Mcnair

Yuxuan Zhao, Kaisheng Zhu, Yefei Zhang et al.

The costly provision of public goods serves as a model problem for the evolution of cooperative behavior, presenting a social dilemma between the collective benefits of shared resources and the individual incentive to free-ride in resource production. The spatial structure of populations can also impact cooperation over public goods, as diffusion of public goods and intentional motion of individuals towards regions with greater resources can interact with population and public goods dynamics to produce heterogeneous patterns in the spatial distribution of strategies and resources. In this paper, we build off a model introduced by Young and Belmonte for the reaction dynamics of interacting individuals and explicit public good, deriving a system of PDEs that describes the spatial profiles of strategies and the public good in the presence of both diffusive motion of individuals and resources and chemotaxis-like directed motion of individuals in response to gradients in the concentration of public goods. Through linear stability analysis, we show that spatial patterns in strategic and public goods profiles can emerge due to either Turing instability with high defector diffusivity or a directed-motion instability through strong sensitivity of cooperators towards increasing resource concentration. We further explore the emergent spatial patterns with a mix of weakly nonlinear stability analysis and numerical simulation, showing that diffusion-driven instability appears to increase cooperation and public goods across the spatial domain, while directed motion of cooperators towards regions with great public goods provision tends to decrease cooperation and environmental quality across the environment.

Filip Bialy, Mark Elliot, Robert Meckin

This paper offers a domain-mediated comparative review of 251 studies on public attitudes toward AI, published between 2011 and 2025. Drawing on a systematic literature review, we analyse how different factors including perceived benefits and concerns (or risks) shape public acceptance of - or resistance to - artificial intelligence across domains and use-cases, including healthcare, education, security, public administration, generative AI, and autonomous vehicles. The analysis highlights recurring patterns in individual, contextual, and technical factors influencing perception, while also tracing variations in institutional trust, perceived fairness, and ethical concerns. We show that the public perception in AI is shaped not only by technical design or performance but also by sector-specific considerations as well as imaginaries, cultural narratives, and historical legacies. This comparative approach offers a foundation for developing more tailored and context-sensitive strategies for responsible AI governance.

Yukun Zhang, TianYang Zhang

This paper conceptualizes Large Language Models (LLMs) as a form of mixed public goods within digital infrastructure, analyzing their economic properties through a comprehensive theoretical framework. We develop mathematical models to quantify the non-rivalry characteristics, partial excludability, and positive externalities of LLMs. Through comparative analysis of open-source and closed-source development paths, we identify systematic differences in resource allocation efficiency, innovation trajectories, and access equity. Our empirical research evaluates the spillover effects and network externalities of LLMs across different domains, including knowledge diffusion, innovation acceleration, and industry transformation. Based on these findings, we propose policy recommendations for balancing innovation incentives with equitable access, including public-private partnership mechanisms, computational resource democratization, and governance structures that optimize social welfare. This interdisciplinary approach contributes to understanding the economic nature of foundation AI models and provides policy guidance for their development as critical digital infrastructure

Gui Zhang, Xiaojin Xiong, Bin Pin et al.

In real-world social and economic systems, the provisioning of public goods generally entails continuous interactions among individuals, with decisions to cooperate or defect being influenced by dynamic factors such as timing, resource availability, and the duration of engagement. However, the traditional public goods game ignores the asynchrony of the strategy adopted by players in the game. To address this problem, we propose a spatial public goods game that integrates an M/M/1 queueing system to simulate the dynamic flow of player interactions. We use a birth-death process to characterize the stochastic dynamics of this queueing system, with players arriving following a Poisson process and service times being exponentially distributed under a first-come-first-served basis with finite queue capacity. We also incorporate reputation so that players who have cooperated in the past are more likely to be chosen for future interactions. Our research shows that a high arrival rate, low service rate, and the reputation mechanism jointly facilitate the emergence of cooperative individuals in the network, which thus provides an interesting and new perspective for the provisioning of public goods.

Fanjun Bu, Kerstin Fischer, Wendy Ju

In this work, we analyze video data and interviews from a public deployment of two trash barrel robots in a large public space to better understand the sensemaking activities people perform when they encounter robots in public spaces. Based on an analysis of 274 human-robot interactions and interviews with N=65 individuals or groups, we discovered that people were responding not only to the robots or their behavior, but also to the general idea of deploying robots as trashcans, and the larger social implications of that idea. They wanted to understand details about the deployment because having that knowledge would change how they interact with the robot. Based on our data and analysis, we have provided implications for design that may be topics for future human-robot design researchers who are exploring robots for public space deployment. Furthermore, our work offers a practical example of analyzing field data to make sense of robots in public spaces.

Ilaria Armaroli, Mehtap Akgüç

Purpose This study explores how social partners contribute to the successful return to work (RTW) of individuals affected by chronic diseases, employing the framework of actor-centred institutionalism.Design/methodology/approach This paper adopts a comparative case study methodology to assess the role of social partners in the workplace (re-)integration of people with chronic disease in Belgium and Italy, both of which represent well-developed industrial relations systems yet having different institutional and policy frameworks on RTW.Findings Institutional factors are found to affect the type and degree of social partners' commitment and contribution to RTW. Differences in their commitment can be explained by their varied degrees of integration in public policy formation, which explain their different preferred stages of interactions in this field: national tripartite social dialogue for Belgium; and sectoral collective bargaining for Italy. Unsatisfactory outcomes of social partners’ contribution in facilitating RTW processes are attributed to the fragmentation of the legal framework and uneven development of collective bargaining in Italy. In Belgium, the authors find the presence of cumbersome RTW procedures downplaying the role of the worker representative.Originality/value This paper adds empirical evidence to the limited literature on the role of social partners in facilitating RTW and sheds light on how to improve the current policy context. It suggests involving the social partners in the development of a comprehensive public policy framework, which should allow for an early, flexible and multi-stakeholder (re-)integration procedure following chronic disease.

Alexandre Afonso, Maximilian Kiecker, Pedro Goulart

This paper provides a quantitative assessment of the political and structural determinants of social dialogue in 25 European countries between 1980 and 2018 using a measure of social dialogue based on an original survey of industrial relations and social policy experts. We assess hypotheses on the role of structural (unionisation, employer organisation) and political (government partisanship, government strength) factors on the extent of cooperation between governments, trade unions and employers in public policymaking. We find a declining trend in the overall extent of social dialogue in the countries surveyed. Using panel regressions, we show that higher levels of social dialogue are more prevalent among governments where there is a balance of power between right‐wing and left‐wing parties, and thus where unions and employers can act as ‘brokers’ between left and right parties. We find no association between most structural factors (unionisation, collective bargaining coverage, employer organisation) and levels of social dialogue.

Enayat Ullah, Michael Menart, Raef Bassily et al.

We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data. For complete/labeled public data, we show that any $(ε,δ)$-PA-DP has excess risk $\tildeΩ\big(\min\big\{\frac{1}{\sqrt{n_{\text{pub}}}},\frac{1}{\sqrt{n}}+\frac{\sqrt{d}}{nε} \big\} \big)$, where $d$ is the dimension, ${n_{\text{pub}}}$ is the number of public samples, ${n_{\text{priv}}}$ is the number of private samples, and $n={n_{\text{pub}}}+{n_{\text{priv}}}$. These lower bounds are established via our new lower bounds for PA-DP mean estimation, which are of a similar form. Up to constant factors, these lower bounds show that the simple strategy of either treating all data as private or discarding the private data, is optimal. We also study PA-DP supervised learning with \textit{unlabeled} public samples. In contrast to our previous result, we here show novel methods for leveraging public data in private supervised learning. For generalized linear models (GLM) with unlabeled public data, we show an efficient algorithm which, given $\tilde{O}({n_{\text{priv}}}ε)$ unlabeled public samples, achieves the dimension independent rate $\tilde{O}\big(\frac{1}{\sqrt{n_{\text{priv}}}} + \frac{1}{\sqrt{n_{\text{priv}}ε}}\big)$. We develop new lower bounds for this setting which shows that this rate cannot be improved with more public samples, and any fewer public samples leads to a worse rate. Finally, we provide extensions of this result to general hypothesis classes with finite fat-shattering dimension with applications to neural networks and non-Euclidean geometries.

Jean Marie Tshimula, Mitterrand Kalengayi, Dieumerci Makenga et al.

Artificial Intelligence (AI) is revolutionizing various fields, including public health surveillance. In Africa, where health systems frequently encounter challenges such as limited resources, inadequate infrastructure, failed health information systems and a shortage of skilled health professionals, AI offers a transformative opportunity. This paper investigates the applications of AI in public health surveillance across the continent, presenting successful case studies and examining the benefits, opportunities, and challenges of implementing AI technologies in African healthcare settings. Our paper highlights AI's potential to enhance disease monitoring and health outcomes, and support effective public health interventions. The findings presented in the paper demonstrate that AI can significantly improve the accuracy and timeliness of disease detection and prediction, optimize resource allocation, and facilitate targeted public health strategies. Additionally, our paper identified key barriers to the widespread adoption of AI in African public health systems and proposed actionable recommendations to overcome these challenges.

Ergon Cugler de Moraes Silva, Jose Carlos Vaz

This study investigates the impact of disinformation on public policies. Using 28 sets of keywords in eight databases, a systematic review was carried out following the Prisma 2020 model (Page et al., 2021). After applying filters and inclusion and exclusion criteria to 4,128 articles and materials found, 46 publications were analyzed, resulting in 23 disinformation impact categories. These categories were organized into two main axes: State and Society and Actors and Dynamics, covering impacts on State actors, society actors, State dynamics and society dynamics. The results indicate that disinformation affects public decisions, adherence to policies, prestige of institutions, perception of reality, consumption, public health and other aspects. Furthermore, this study suggests that disinformation should be treated as a public problem and incorporated into the public policy research agenda, contributing to the development of strategies to mitigate its effects on government actions.

Rafki Novariza, Tresna Wiwitan

Abstract. Marketing communications is an effort to convey messages or information to the public, especially consumers as targets regarding the existence of products on the market. Conveying a message here means promoting a product to consumers. Here the researcher uses a promotional mix known as the promotional mix, namely advertising, face-to-face selling (personal selling), sales promotion, public relations and publicity (publicity and public relations), and direct marketing (direct marketing). ). This research focuses on the Instagram social media account from Bumi Aki Heritage as marketing communication. This research aims to determine content planning, making photos and captions as well as evaluating the use of Instagram social media. This research uses qualitative methods with a descriptive analysis approach. Researchers used three data collection techniques, namely interviews, observation and literature study. The results of this research show that Bumi Aki Heritage's marketing communications activities in building brand image and brand positioning via Instagram involve all elements of the marketing mix, namely product, price, place and promotion. Where the product concept, price, place and promotional activities carried out can provide a good image for Bumi Aki Heritage. Then, in carrying out marketing communications, Bumi Aki Heritage makes good use of the features on Instagram, such as the Instagram story feature, highlights feature, photo or video upload feature, caption and hashtag features. Abstrak. Komunikasi pemasaran merupakan usaha untuk menyampaikan pesan atau informasi kepada public terutama konsumen sebagai sasaran mengenai keberadaan produk di pasar. Menyampaikan pesan disini maksudnya yaitu mempromosikan suatu produk kepada konsumen. Disini peneliti menggunakan bauran promosi yang dikenal sebagai ( promotional mix) yaitu iklan (advertising), penjualan tatap muka (personal selling), promosi penjualan (sales promotion), hubungan masyarakat dan publisitas (publicity and public relations), serta pemasaran langsung (direct marketing). Penelitian ini fokus di akun media sosial Instagram dari Bumi Aki Heritage sebagai komunikasi pemasaran. Penelitian ini bertujuan untuk mengetahui perencanaan konten, pembuatan foto dan caption serta evalusi dari penggunaan media sosial Instagram tersebut. Penelitian ini menggunakan metode kualitati dengan pendekatan analisis deskriptif. Peneliti menggunakan tiga Teknik pengumpulan data, yaitu wawancara, observasi dan studi kepustakaan. Hasil penelitian ini didapat menunjukan bahwa kegiatan komunikasi pemasaran bumi Aki Heritage dalam membangun brand image dan brand positioning melalui Instagram melibatkan semua elemen bauran pemasaran atau marketing mix, yakni product, price, place, dan promotion. Dimana konsep produk, harga, tempat, dan kegiatan promosi yang dilakukan, dapat memberikan citra yang baik bagi Bumi Aki Heritage. Kemudian dalam melakukan komunikasi pemasaran, Bumi Aki Heritage memanfaatkan dengan baik fitur-fitur yang ada di Instagram, seperti fitur Instagram story, fitur highlights, fitur unggahan foto atau video, fitur caption dan hastag.

Amjid Khan, Muhammad Zareef, P. Ahmed

Abstract Public libraries (PLs) aim to meet the information needs of their local communities and play a vital role in the development of a nation. Comprehensive published literature is available on PLs in Pakistan and could explore the challenges encountered by these PLs in the country through literature review. Thus, to fill this gap in existing literature, a review of literature was conducted to explore the status, issues, and perspectives of the PLs in Pakistan since its partition in 1947. The researchers reviewed library and information science (LIS) literature published on the subject topics. Various electronic data sources were searched such as Taylor and Francis, Emerald Insight, EBSCOhost, Science Direct, Web of Science (WoS), Scopus, Library and Information Science Abstracts (LISA), Library, Information Science & Technology Abstracts (LISTA), and Google Scholar. Similarly, the profiles of prominent Pakistani LIS scholars were also consulted for the said purpose. A search string was formulated to get precise results with some limitations on extracting required data. Peer-reviewed studies published from 1947 to 2021 in the English language were included in present study, which extracted 74 citations. After deleting duplicates from titles and abstracts, a full-text assessment was done. Finally, 27 studies were included, which matched the search criteria and explored various issues of the PLs in Pakistan. The findings of this study reveal that PLs in Pakistan are facing several challenges such as nonexistence of appropriate planning, absence of library legislation, lack of ICT application in libraries, absence of policy regarding public relations and publicity (PRP) of library resources and services. Other issues are lack of proper service structure for LIS professionals, as a deficiency in providing proper LIS continuing education and training opportunities to LIS professionals, insufficient human resources, the bureaucratic administrative structure of public libraries, shortage of sufficient funds and financial independence. The highlighted issues and challenges may serve as a guide for public library policy and decision-makers in reforming their present policies, or in articulating a modern public library system in Pakistan. This study suggests several recommendations which would help the competent authorities and policymakers to transform the PLs in Pakistan from neglected to indispensable community learning and information resource centers. This is a first study of its kind in Pakistan, and it is hoped that the findings will serve as a springboard for future research in the LIS field and contribute to the literature on the PLs in Pakistan.

T. Bajenova

Shannon B. Harper, Eric S. Weber

Automated decision-making systems are being increasingly deployed and affect the public in a multitude of positive and negative ways. Governmental and private institutions use these systems to process information according to certain human-devised rules in order to address social problems or organizational challenges. Both research and real-world experience indicate that the public lacks trust in automated decision-making systems and the institutions that deploy them. The recreancy theorem argues that the public is more likely to trust and support decisions made or influenced by automated decision-making systems if the institutions that administer them meet their fiduciary responsibility. However, often the public is never informed of how these systems operate and resultant institutional decisions are made. A ``black box'' effect of automated decision-making systems reduces the public's perceptions of integrity and trustworthiness. The result is that the public loses the capacity to identify, challenge, and rectify unfairness or the costs associated with the loss of public goods or benefits. The current position paper defines and explains the role of fiduciary responsibility within an automated decision-making system. We formulate an automated decision-making system as a data science lifecycle (DSL) and examine the implications of fiduciary responsibility within the context of the DSL. Fiduciary responsibility within DSLs provides a methodology for addressing the public's lack of trust in automated decision-making systems and the institutions that employ them to make decisions affecting the public. We posit that fiduciary responsibility manifests in several contexts of a DSL, each of which requires its own mitigation of sources of mistrust. To instantiate fiduciary responsibility, a Los Angeles Police Department (LAPD) predictive policing case study is examined.

Venance Riblier

This paper investigates how the cost of public debt shapes fiscal policy and its effect on the economy. Using U.S. historical data, I show that when servicing the debt creates a fiscal burden, the government responds to spending shocks by limiting debt issuance. As a result, the initial shock triggers only a limited increase in public spending in the short run, and even leads to spending reversal in the long run. Under these conditions, fiscal policy loses its ability to stimulate economic activity. This outcome arises as the fiscal authority limits its own ability to borrow to ensure public debt sustainability. These findings are robust to several identification and estimation strategies.

Paolo Ciancarini, Raffaele Giancarlo, Gennaro Grimaudo

Digital Transformation (DT) is the process of integrating digital technologies and solutions into the activities of an organization, whether public or private. This paper focuses on the DT of public sector organizations, where the targets of innovative digital solutions are either the citizens or the administrative bodies or both. This paper is a guided tour for Computer Scientists, as the digital transformation of the public sector involves more than just the use of technology. While technological innovation is a crucial component of any digital transformation, it is not sufficient on its own. Instead, DT requires a cultural, organizational, and technological shift in the way public sector organizations operate and relate to their users, creating the capabilities within the organization to take full advantage of any opportunity in the fastest, best, and most innovative manner in the ways they operate and relate to the citizens. Our tutorial is based on the results of a survey that we performed as an analysis of scientific literature available in some digital libraries well known to Computer Scientists. Such tutorial let us to identify four key pillars that sustain a successful DT: (open) data, ICT technologies, digital skills of citizens and public administrators, and agile processes for developing new digital services and products. The tutorial discusses the interaction of these pillars and highlights the importance of data as the first and foremost pillar of any DT. We have developed a conceptual map in the form of a graph model to show some basic relationships among these pillars. We discuss the relationships among the four pillars aiming at avoiding the potential negative bias that may arise from a rendering of DT restricted to technology only. We also provide illustrative examples and highlight relevant trends emerging from the current state of the art.

Yan Jiang, Ruihong Qiu, Yi Zhang et al.

As social media becomes increasingly popular, more and more activities related to public health emerge. Current techniques for public health analysis involve popular models such as BERT and large language models (LLMs). However, the costs of training in-domain LLMs for public health are especially expensive. Furthermore, such kinds of in-domain datasets from social media are generally imbalanced. To tackle these challenges, the data imbalance issue can be overcome by data augmentation and balanced training. Moreover, the ability of the LLMs can be effectively utilized by prompting the model properly. In this paper, a novel ALEX framework is proposed to improve the performance of public health analysis on social media by adopting an LLMs explanation mechanism. Results show that our ALEX model got the best performance among all submissions in both Task 2 and Task 4 with a high score in Task 1 in Social Media Mining for Health 2023 (SMM4H)[1]. Our code has been released at https:// github.com/YanJiangJerry/ALEX.

Shai Ben-David, Alex Bie, Clément L. Canonne et al.

We study the problem of private distribution learning with access to public data. In this setup, which we refer to as public-private learning, the learner is given public and private samples drawn from an unknown distribution $p$ belonging to a class $\mathcal Q$, with the goal of outputting an estimate of $p$ while adhering to privacy constraints (here, pure differential privacy) only with respect to the private samples. We show that the public-private learnability of a class $\mathcal Q$ is connected to the existence of a sample compression scheme for $\mathcal Q$, as well as to an intermediate notion we refer to as list learning. Leveraging this connection: (1) approximately recovers previous results on Gaussians over $\mathbb R^d$; and (2) leads to new ones, including sample complexity upper bounds for arbitrary $k$-mixtures of Gaussians over $\mathbb R^d$, results for agnostic and distribution-shift resistant learners, as well as closure properties for public-private learnability under taking mixtures and products of distributions. Finally, via the connection to list learning, we show that for Gaussians in $\mathbb R^d$, at least $d$ public samples are necessary for private learnability, which is close to the known upper bound of $d+1$ public samples.

Sicong Xie, Binbin Hu, Fengze Li et al.

Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems. With the real data in Alipay, a feeds-like scenario for O2O services, we find that recurrence based temporal patterns and position biases commonly exist in our scenarios, which seriously threaten the recommendation effectiveness. To this end, we propose COUPA, an industrial system targeting for characterizing user preference with following two considerations: (1) Time aware preference: we employ the continuous time aware point process equipped with an attention mechanism to fully capture temporal patterns for recommendation. (2) Position aware preference: a position selector component equipped with a position personalization module is elaborately designed to mitigate position bias in a personalized manner. Finally, we carefully implement and deploy COUPA on Alipay with a cooperation of edge, streaming and batch computing, as well as a two-stage online serving mode, to support several popular recommendation scenarios. We conduct extensive experiments to demonstrate that COUPA consistently achieves superior performance and has potential to provide intuitive evidences for recommendation