The project-based approach to strategic management of the socioeconomic development of the Russian Federation has proven itself effective. As a result, second-generation eco nomic crisis projects for 2025 have been adopted and are being implemented to replace the national projects completed in 2024. An analysis of the progress and results of the national projects revealed that, in a number of cases, there is inefficiency in both the achievement of project objectives and the expenditure of budgetary resources. The following factors have been identified as the causes of this situation: lagging methodologies for statistical sup port of national projects, insufficient methods for developing the implementation of projects, and weak methodological support for assessing the effectiveness of national projects. The purpose of this article is to develop methodological and methodological support for national project implementation indicators designed to improve their effectiveness. The article pro poses and tests a proprietary methodology for assessing national project implementation indicators, which can be used in practice to ensure the quality and comprehensiveness of their implementation.
Political institutions and public administration (General)
Loretta Gasparini, Nitya Phillipson, Daniel Capurro
et al.
The use of large language models (LLMs) has exploded since November 2022, but there is sparse evidence regarding LLM use in health, medical, and research contexts. We aimed to summarise the current uses of and attitudes towards LLMs across our campus’ clinical, research, and teaching sites. We administered a survey about LLM uses and attitudes. We conducted summary quantitative analysis and inductive qualitative analysis of free text responses. In August–September 2023, we circulated the survey amongst all staff and students across our three campus sites (approximately n = 7500), comprising a paediatric academic hospital, research institute, and paediatric university department. We received 281 anonymous survey responses. We asked about participants’ knowledge of LLMs, their current use of LLMs in professional or learning contexts, and perspectives on possible future uses, opportunities, and risks of LLM use. Over 90% of respondents have heard of LLM tools and about two-thirds have used them in their work on our campus. Respondents reported using LLMs for various uses, including generating or editing text and exploring ideas. Many, but not necessarily all, respondents seem aware of the limitations and potential risks of LLMs, including privacy and security risks. Various respondents expressed enthusiasm about the opportunities of LLM use, including increased efficiency. Our findings show LLM tools are already widely used on our campus. Guidelines and governance are needed to keep up with practice. Insights from this survey were used to develop recommendations for the use of LLMs on our campus.
Information technology, Political institutions and public administration (General)
El artículo tiene por objetivo analizar los principales factores que dificultan que las entidades del Estado operen con eficiencia, transparencia, respeto por los principios éticos y orientación a la generación de valor público. A partir de un análisis crítico de la situación actual del país y de la revisión documental de informes institucionales, estudios especializados y evidencia comparada, se identifican desafíos estructurales y jurídicos que afectan el desempeño del aparato estatal. Entre ellos destacan la estructura organizacional jerarquizada, sistemas informáticos obsoletos, una gestión ineficaz, la limitada profesionalización del recurso humano y una infraestructura insuficiente para atender las necesidades de la población.
El análisis se organiza en cuatro ejes: (i) la fragmentación normativa y su impacto en la coherencia institucional; (ii) la debilidad de los mecanismos de control y rendición de cuentas; (iii) los problemas estructurales de la gestión pública en el Perú; y (iv) la participación ciudadana y la democratización del proceso público. El artículo adopta un enfoque jurídico-descriptivo y reflexivo, basado en el análisis documental y comparado de normativa, informes institucionales y literatura especializada, seleccionados conforme a criterios de relevancia, actualidad y pertinencia temática.
A partir del análisis realizado, se plantean lineamientos orientados a fortalecer las organizaciones estatales y avanzar hacia un Estado democrático, funcional y orientado a la generación de valor público, promoviendo una administración pública ética, íntegra, transparente y centrada en el servicio a la ciudadanía.
Political institutions and public administration (General)
Ramesh Soni, Kurt Schimmel, Frederick Slack
et al.
This paper examines the intersection between entrepreneurship government policy and managerial theory. The context chosen for this study is India. India has experienced a significant global geopolitical shift that is coinciding with India’s domestic policy reforms and notable domestic initiatives. Since 2014, India’s entrepreneurial ecosystem has seen a significant increase in the number of startups and unicorns. This paper presents arguments that the confluence of global realignments, such as the diversification of supply chains away from China and increasing interest in the Indo-Pacific region, along with domestic initiatives like “Make in India”, “Startup India”, and digitalization drives, along with massive investments in infrastructure improvements, have made India a desirable destination for entrepreneurial activity. By examining these factors through the lens of three theories—resource-based view, global value chain, and innovation ecosystem theory—this paper identifies key opportunities and challenges for entrepreneurs across various sectors. It is hoped that this research will contribute to a deeper understanding of India’s evolving entrepreneurial landscape. In addition, entrepreneurs, policymakers, and investors can benefit from this article to understand the opportunities and challenges India poses in order to contribute to India’s continued economic growth and its emergence as a global entrepreneurial powerhouse. Finally, this paper helps to bridge the gap between economic policy and management theory.
Political institutions and public administration (General)
Money laundering is the process that intends to legalize the income derived from illicit activities, thus facilitating their entry into the monetary flow of the economy without jeopardizing their source. It is crucial to identify such activities accurately and reliably in order to enforce anti-money laundering (AML). Despite considerable efforts to AML, a large number of such activities still go undetected. Rule-based methods were first introduced and are still widely used in current detection systems. With the rise of machine learning, graph-based learning methods have gained prominence in detecting illicit accounts through the analysis of money transfer graphs. Nevertheless, these methods generally assume that the transaction graph is centralized, whereas in practice, money laundering activities usually span multiple financial institutions. Due to regulatory, legal, commercial, and customer privacy concerns, institutions tend not to share data, restricting their utility in practical usage. In this paper, we propose the first algorithm that supports performing AML over multiple institutions while protecting the security and privacy of local data. To evaluate, we construct Alipay-ECB, a real-world dataset comprising digital transactions from Alipay, the world's largest mobile payment platform, alongside transactions from E-Commerce Bank (ECB). The dataset includes over 200 million accounts and 300 million transactions, covering both intra-institution transactions and those between Alipay and ECB. This makes it the largest real-world transaction graph available for analysis. The experimental results demonstrate that our methods can effectively identify cross-institution money laundering subgroups. Additionally, experiments on synthetic datasets also demonstrate that our method is efficient, requiring only a few minutes on datasets with millions of transactions.
In this paper, we provide a novel measure for greenwashing -- i.e., climate-related misinformation -- that shows how polluting companies can use social media advertising related to climate change to redirect criticism. To do so, we identify greenwashing content in 11 million social-political ads in Meta's Ad Targeting Datset with a measurement technique that combines large language models, human coders, and advances in Bayesian item response theory. We show that what is called greenwashing has diverse actors and components, but we also identify a very pernicious form, which we call political greenwashing, that appears to be promoted by fossil fuel companies and related interest groups. Based on ad targeting data, we show that much of this advertising happens via organizations with undisclosed links to the fossil fuel industry. Furthermore, we show that greenwashing ad content is being micro-targeted at left-leaning communities with fossil fuel assets, though we also find comparatively little evidence of ad targeting aimed at influencing public opinion at the national level.
Joshua Tan, Nicholas Vincent, Katherine Elkins
et al.
Open source projects have made incredible progress in producing transparent and widely usable machine learning models and systems, but open source alone will face challenges in fully democratizing access to AI. Unlike software, AI models require substantial resources for activation -- compute, post-training, deployment, and oversight -- which only a few actors can currently provide. This paper argues that open source AI must be complemented by public AI: infrastructure and institutions that ensure models are accessible, sustainable, and governed in the public interest. To achieve the full promise of AI models as prosocial public goods, we need to build public infrastructure to power and deliver open source software and models.
Douglas Markant, Subham Sah, Alireza Karduni
et al.
Political sectarianism is fueled in part by misperceptions of political opponents: People commonly overestimate the support for extreme policies among members of the other party. Research suggests that correcting partisan misperceptions by informing people about the actual views of outparty members may reduce one's own expressed support for political extremism, including partisan violence and anti-democratic actions. The present study investigated how correction effects depend on different representations of outparty views communicated through data visualizations. We conducted an experiment with U.S. based participants from Prolific (N=239 Democrats, N=244 Republicans). Participants made predictions about support for political violence and undemocratic practices among members of their political outparty. They were then presented with data from an earlier survey on the actual views of outparty members. Some participants viewed only the average response (Mean-Only condition), while other groups were shown visual representations of the range of views from 75% of the outparty (Mean+Interval condition) or the full distribution of responses (Mean+Points condition). Compared to a control group that was not informed about outparty views, we observed the strongest correction effects among participants in the Mean-only and Mean+Points condition, while correction effects were weaker in the Mean+Interval condition. In addition, participants who observed the full distribution of out-party views (Mean+Points condition) were most accurate at later recalling the degree of support among the outparty. Our findings suggest that data visualizations can be an important tool for correcting pervasive distortions in beliefs about other groups. However, the way in which variability in outparty views is visualized can significantly shape how people interpret and respond to corrective information.
AbstractGender diversity in leadership positions may not always bring desirable outcomes for an organization as diversity researchers have argued. Female leaders are less likely to contribute to effectiveness of their organization when it is male‐dominated and has strong masculine culture. We tested a nonlinear relationship between gender diversity at the top and organizational performance and the moderating effect of a female critical mass in an organization. Time‐series data were collected from state‐owned enterprises and quasi‐governmental organizations affiliated with the Korean government and were analyzed through fixed‐effects panel regressions. The results show that gender diversity on executive boards has an inverted U‐shaped relationship with organizational performance. The positive effect of gender diversity on executive boards on organizational performance increases up to a certain level, beyond which the diversity effect turns negative. Curvilinear relationships were found to be flatter in more gender‐balanced organizational settings, suggesting that the negative effect of gender diversity at the top is likely to increase to a lesser extent than in male‐skewed settings.
Amir Hossein Abdallahzadeh, Seyyed Mohammad Moghimi, Hossein Imani
Objective
One of the most significant topics discussed in management and leadership literature is the concept of self-sacrifice. Due to its numerous positive implications, organizations need to develop leadership styles based on self-sacrificial behaviors. Despite various examples and instances of self-sacrifice exhibited by managers and leaders, research on self-sacrifice and its leadership implications has been neglected. Therefore, further research in this area can illuminate the dimensions and aspects of self-sacrificial behaviors in organizations.
Methods
This study employs the Interpretive Structural Modeling (ISM) method. It is an applied research project that utilizes interviews as the primary data collection method.
Results
The findings of the research indicate that the conceptual model of self-sacrificial behaviors in organizations consists of eleven components: "positive self-concept," "resilience," "social representation," "motivation to serve," "empathy and compassion," "awareness and knowledge," "goal orientation and idealism," "collective identity," "social learning and social contagion," "core values," and "crisis." According to the findings, the two dimensions of "awareness and knowledge" and "collective identity" are the foundational components of the model, as they influence all other components and have a two-way relationship with each other. This means that, in addition to being influencing factors for other components, they also impact each other. Additionally, based on the model, the components of "empathy and compassion" and "social learning and social contagion" are ranked next. These two components also have a mutual relationship with each other and are further influenced by the "crisis" component, which ranks below them. In fact, the critical condition, in addition to affecting the higher-level components, also impacts the lower-level components, indicating the significant influence of this variable in the model. The "motivation to serve" component is placed at the fifth level of the model. As shown by the direction of the arrows, this component is dependent on the three components of critical conditions, core values, and goal orientation and idealism. This means that the motivation to serve, as one of the antecedents of altruistic behavior, is influenced by the occurrence of critical conditions and the presence of core values, goals, and ideals of the individual. The remaining three components in the model—positive self-concept, resilience, and social representation—have the least influence and the most dependence on other components, indicating that they are more influenced by other components in the model. The "social representation" component is the most dependent in the model, meaning that a person's desire for social expressiveness is reliant on all other components except resilience, as resilience does not affect social expressiveness.
Conclusion
Based on the results of the study using ISM, two components are identified in the linkage region: "empathy and compassion" and "awareness and knowledge." These components are considered dynamic, meaning that any change in them can impact the entire system. The independent region includes five components: "goal orientation and idealism," "collective identity," "social learning and social contagion," "core values," and "crisis," indicating their strong influence and guiding role in the model. Additionally, the "social representation" component is placed in the dependence region, signifying its high reliance on other components.
Political institutions and public administration (General)
Supriti Vijay, Aman Priyanshu, Ashique R. KhudaBukhsh
In an era where societal narratives are increasingly shaped by algorithmic curation, investigating the political neutrality of LLMs is an important research question. This study presents a fresh perspective on quantifying the political neutrality of LLMs through the lens of abstractive text summarization of polarizing news articles. We consider five pressing issues in current US politics: abortion, gun control/rights, healthcare, immigration, and LGBTQ+ rights. Via a substantial corpus of 20,344 news articles, our study reveals a consistent trend towards pro-Democratic biases in several well-known LLMs, with gun control and healthcare exhibiting the most pronounced biases (max polarization differences of -9.49% and -6.14%, respectively). Further analysis uncovers a strong convergence in the vocabulary of the LLM outputs for these divisive topics (55% overlap for Democrat-leaning representations, 52% for Republican). Being months away from a US election of consequence, we consider our findings important.
Cél: Jelen írás bemutatja az 1914. április 14–18. között Monacóban megrendezett I. Nemzetközi Igazságügyi Rendőrkongresszust és annak eredményeit, valamint az Interpol Közgyűlésének százéves évfordulóján megemlékezik a 2014. november 3–7. között Monacóban tartott 83. Közgyűlésről.
Módszertan: A cikk megírásához monacói levéltári dokumentumokat használt fel a szerző. Ezek közül néhány közzétételre is kerül.
Megállapítások: A 20. század elejétől kezdve a Monacói Hercegség egy előrelátó államfő, I. Albert herceg vezetésével erőteljesen részt vett a különböző országok igazságügyi rendőrségei közötti nemzetközi együttműködés kialakítását és a bűnügyi információcserék javítását célzó intézkedések végrehajtásában, hogy megkönnyítse a bűnözők felkutatását és letartóztatását. E célból Monaco 1914-ben megszervezte az első kongresszust, amely rendőröket és jogászokat látott vendégül, akiknek az volt a feladatuk, hogy javaslatokat dolgozzanak ki az új igények kielégítésére, és mintegy húsz ország képviselőit közvetlen kapcsolatba hozzák egymással. Azonban az első világháború kitörése megszakította a megkezdett nemzetközi eszmecserék megerősítésének folyamatát, amelynek egy 1916-ra tervezett második állomáshoz kellett volna vezetnie, és amely végül csak 1923-ban folytatódott.
Érték: 2023-ban lesz a Nemzetközi Bűnügyi Rendőrség Szervezete megalakulásának századik évfordulója, amelyből a Nemzetközi Bűnügyi Rendőrségi Szervezet – Interpol – lett. Történelmi szempontból érdekes megvizsgálni a bűnözés elleni küzdelemre szakosodott nemzetközi intézmény megalapításának kezdeteit, és felidézni, hogy ez a konstrukció egy régebbi, a 20. század elején kezdeményezett gondolkodás eredménye, amely először egy 1914-es monacói kezdeményezésen keresztül valósult meg (amelyről 2014-ben szintén Monacóban emlékeztek meg). Az akkori szakértők által a hercegség meghívására elfogadott és ebben a közleményben bemutatott határozatok hangsúlyozzák az általuk azonosított problémák pontosságát, valamint a javasolt megoldások relevanciáját, amelyek közül néhány ma is érvényes.
Political institutions and public administration (General)
Entrepreneurship represents an innovative and optimistic perspective for economic and social development. By creating new businesses and jobs, entrepreneurship contributes to the reduction of unemployment and it helps increase the standard of living. Entrepreneurship can also be a source of inspiration for young people and the opportunity to reach their potential and pursue their passion. In this regard, the government and the decision-makers should encourage and support entrepreneurial activities through appropriate programs and initiatives. The present study is based on a bibliometric analysis of the entrepreneurial profile. The bibliometric analysis was made on the basis of 1277 scientific articles extracted from the Web of Science database, on the topic of entrepreneurial profile. Data processing was carried out with the help of the VOSviewer software. The results of the analysis highlight the evolution of the subject over the 1991-2022 period, providing a significant picture of the entrepreneurial profile for the researchers in the field.
Political institutions and public administration (General)
Under conditions of uncertainty caused by sanctions and geopolitical changes, the correct choice of both strategic development priorities and management tools for their implementation becomes important. Given the global trend towards digitalization and the growing supply from software developers, it is necessary to understand clearly, which software products and for what purposes can be used effectively at specific enterprises. The challenge here is that due to instability, sanctions pressure, and withdrawals of foreign software from the Russian market, enterprises are forced to change business and technological processes, and to look for new tools for implementing development strategies. The purpose of the article is to determine the basic principles for choosing strategic management tools and information technologies as elements of strategic management at enterprises in the context of digital transformation. The article shows that due to the ongoing digitalization of industrial enterprises any tools for implementing the strategies must be considered within the framework of an automated process management model. It can be based on various information technologies or platform solutions, depending on the coverage of enterprise business processes, and the readiness to adopt digital technologies in strategic and operational management. The modern trend is to implement strategic development directions, relying on the internal ecosystem of the enterprise. Making the right choice of information technology within an enterprise ecosystem requires that technology investments are aligned with the company’s business goals, adaptable to changing needs, can integrate with existing systems, are user-friendly, secure, and provide a short-term return on investment. Ultimately, these principles enable businesses to successfully navigate the digital transformation journey and leverage technology as a strategic enabler for sustainable growth.
Political institutions and public administration (General)
Alyvia Walters, Tawfiq Ammari, Kiran Garimella
et al.
Visual culture has long been deployed by actors across the political spectrum as tools of political mobilization, and have recently incorporated new communication tools, such as memes, GIFs, and emojis. In this study, we analyze the top-circulated Facebook memes relating to critical race theory (CRT) from May 2021 - May 2022 to investigate their visual and textual appeals. Using image clustering techniques and critical discourse analysis, we find that both pro- and anti-CRT memes deploy similar rhetorical tactics to make bifurcating arguments, most of which do not pertain to the academic formulations of CRT. Instead, these memes manipulate definitions of racism and antiracism to appeal to their respective audiences. We argue that labeling such discursive practices as simply a symptom of "post-truth" politics is a potentially unproductive stance. Instead, theorizing the knowledge-building practices of these memes through a lens of political epistemology allows us to understand how they produce meaning.
Carolina Luque, Isabella Agudelo, Kevin Leal
et al.
Statistical analysis of social networks is a predominant methodology in political science research. In this article we implement network methods to characterize the presidential inauguration speech and identify political communities in Colombia. We propose an empirical approach to analize the discursive structure of the heads of state and the configuration of alliance and work relationships between prominent figures of Colombian politics. Thus, we implement network methods from two perspectives, words and political actors. We conclude on the relevance of social network statistics to identify frequent and important terms in the communicative action of president figures, and to examine cohesion such discourses. Finally, we distinguish notable actors in the consolidation of working relationships and alliances as well as political communities.
The 'intuitive' trust people feel when encountering robots in public spaces is a key determinant of their willingness to cooperate with these robots. We conducted four experiments to study this topic in the context of peacekeeping robots. Participants viewed scenarios, presented as static images or animations, involving a robot or a human guard performing an access-control task. The guards interacted more or less politely with younger and older male and female people. Our results show strong effects of the guard's politeness. Age and sex of the people interacting with the guard had no significant effect on participants' impressions of its attributes. There were no differences between responses to robot and human guards. This study advances the notion that politeness is a crucial determinant of people's perception of peacekeeping robots.
Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. In light of these limitations, we propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning and incorporate textual cues as paragraph-level labels. Specifically, we firstly generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles. Finally, we adopt relational graph neural networks for graph-level representation learning and conduct political perspective detection. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on two benchmark datasets. We further examine the effect of knowledge walks and textual cues and how they contribute to our approach's data efficiency.
Yujian Liu, Xinliang Frederick Zhang, David Wegsman
et al.
Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.
Geographical considerations such as contiguity and compactness are necessary elements of political districting in practice. Yet an analysis of the problem without such constraints yields mathematical insights that can inform real-world model construction. In particular, it clarifies the sharp contrast between proportionality and competitiveness and how it might be overcome in a properly formulated objective function. It also reveals serious weaknesses of the much-discussed efficiency gap as a criterion for gerrymandering.