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This is a sub-national dataset containing data from Indian provinces, combining political party performance in Indian state elections, public good delivery and economic indicators.

After liberalization, business environment changed radically in India. Organizations faced competition and tried to improve their performance. Many organizations tried to change their business processes as well as organizational structure. Information technology played a key role in transforming organizations. Today organizations have become flat but diverse and complex. The objective of the paper is to analyze implications of personality characteristics of employees for designing an appropriate organizational structure for business organizations in India. The paper is based on review of previous research studies in the last decades. Success of any organizational structure depends upon profile of employees. Most organizations expect employees to adjust to their organizational structure. Previous studies on Five Factor Model as well as organizational structure have been reviewed and their implications for designing organizational structure in Indian context have been discussed.

Organizations are fundamentally different from the hierarchical, bureaucratic structures that underlie more traditional organizational theory and research. The paper deals with the fact that culture is omnipotent in shaping the structure of the organisation and structure along with culture is intricately related with the way innovation is managed or implemented in any organisation. This paper is basically a extensive review of papers relating to organisational structure, culture and innovation right from the aspect of how culture shapes structure to how innovation is linked and shaped by both organisational structure and culture. Models by different researchers depicting the relationship between the various aspects of structure culture and innovation are discussed for better understanding.

AbstractWe investigate the statistical learning of nodal attribute functionals in homophily networks using random walks. Attributes can be discrete or continuous. A generalization of various existing canonical models, based on preferential attachment is studied (model class$$\mathscr {P}$$P), where new nodes form connections dependent on both their attribute values and popularity as measured by degree. An associated model class$$\mathscr {U}$$Uis described, which is amenable to theoretical analysis and gives access to asymptotics of a host of functionals of interest. Settings where asymptotics for model class$$\mathscr {U}$$Utransfer over to model class$$\mathscr {P}$$Pthrough the phenomenon of resolvability are analyzed. For the statistical learning, we consider several canonical attribute agnostic sampling schemes such as Metropolis-Hasting random walk, versions of node2vec (Grover and Leskovec, 2016) that incorporate both classical random walk and non-backtracking propensities and propose new variants which use attribute information in addition to topological information to explore the network. Estimators for learning the attribute distribution, degree distribution for an attribute type and homophily measures are proposed. The performance of such statistical learning framework is studied on both synthetic networks (model class$$\mathscr {P}$$P) and real world systems, and its dependence on the network topology, degree of homophily or absence thereof, (un)balanced attributes, is assessed.

AbstractHow do parties in multiethnic societies shape voter attitudes toward female candidates? We address this question, focusing on parties with ideologies that contain ethnonationalist gender norms—patriarchal norms applied to women from an ethnonationalist party's core ethnic constituency. We argue that, while these norms appeal to an ethnonationalist party's base, they also provide informational cues to the party's "non‐core" voters that undermine their support for the party's "core" female candidates. Evidence from an original conjoint survey experiment in the Indian state of Bihar supports our argument; upper‐caste female candidates suffer a support penalty when they are affiliated with the national ruling party, whose ideology prescribes ethnonationalist gender norms targeting its core Hindu upper‐caste constituency. This penalty, we show, is driven by the party's non‐core voters. Our results, which we further bolster using real‐world electoral data, illuminate when and how ethnonationalist gender norms disadvantage elite female candidates.

What factors influence women's political success in ethnically divided societies? Using an original survey experiment in the Indian state of Bihar, supplemented with qualitative interviews, we explore the impact of two factors—intersecting gender and caste identity, and the interaction of campaign appeal with voter experiences of caste discrimination—on women candidates' success in state-level elections. We find, first, that women voters prefer women candidates, and that Scheduled Caste and Muslim voters also prefer candidates from their in-groups. At the same time, we identify evidence of intersectional effects, namely, that Muslim women candidates suffer from a disadvantage vis-a-vis women candidates from other backgrounds. We also show that women voters prefer candidates who offer security, especially when the candidates are women. Finally, we demonstrate that personal experience with caste discrimination increases support for women candidates. These results indicate that voters see women leaders as well-placed to ameliorate their security vulnerabilities.

Studies have found limited evidence consistent with the theory that partisan and like-minded online news exposure have demonstrable effects on political outcomes. Most of this prior research, however, has focused on the particular case of the United States even as concern elsewhere in the world has grown about political parallelism in media content online, which has sometimes been blamed for heightened social divisiveness. This article investigates the impact of online partisan news consumption on voting behavior and social polarization during the 2022 elections in Brazil, a country where the public's ties to political parties have historically been more limited or nonexistent but where ideologically aligned news content online has markedly increased in recent years. Drawing on a unique dataset linking behavioral web-tracking data of 2,200 internet users in Brazil and 4 survey waves with the same respondents, conducted before, during, and after the 2022 presidential elections, we find no significant relationship between the use of partisan media on either vote choice or social polarization overall; however, we do find some weak and inconsistent effects of trust in news moderating the impact of partisan media on social polarization.

Abstract
Electoral misinformation, where citizens believe false or misleading claims about the electoral process and electoral institutions—sometimes actively and strategically spread by political actors—is a challenge to public confidence in elections specifically and democracy more broadly. In this article, we analyze a combination of 42 million clicks in links and apps from behavioral tracking data of 2,200 internet users and a four-wave panel survey to investigate how different kinds of online news and media use relate to beliefs in electoral misinformation during a contentious political period—the 2022 Brazilian presidential elections. We find that, controlling for other factors, using news from legacy news media is associated with belief in fewer claims of electoral misinformation over time. We find null or inconsistent effects for using digital-born news media and various digital platforms, including Facebook and WhatsApp. Furthermore, we find that trust in news plays a significant role as a moderator. Belief in electoral misinformation, in turn, undermines trust in news. Overall, our findings document the important role of the news media as an institution in curbing electoral misinformation, even as they also underline the precarity of trust in news during contentious political periods.

The category of species has remained largely understudied in mainstream gender scholarship. This edition of the Yearbook of Women's History attempts to show how gender history can be enriched through the study of animals. It highlights that the inclusion of nonhuman animals in historical work has the potential to revolutionize the ways we think about gender history. This volume is expansive in more than one way. First, it is global and transhistorical in its outlook, bringing together perspectives from the Global North and the Global South, and moving from the Middle Ages to the contemporary world. Even more importantly for its purposes, a range of animals appear in the contributions: from the smallest insects to great apes, and from 'cute' kittens to riot dogs and lions. The articles collected here reflect the variety of the animal kingdom and of the creative approaches enabled by animal history