This study investigates how institutional origin affects the dot tax haven (DTH) internationalization of Chinese family firms (FFs). Drawing on institutional theory and the mixed gamble perspective, we propose that restructured FFs (RFFs), originating from state-owned enterprises (SOEs), are more likely to engage in DTH internationalization than entrepreneurial FFs directly established by family founders. This propensity is attributed to the institutional legacies inherited from their SOE predecessors, which create a distinct potential gain-loss calculus. Our empirical analysis of publicly listed Chinese FFs from 2012 to 2021 demonstrates that restructured FFs are 30% more likely to use DTH and establish 43% more DTH subsidiaries than entrepreneurial FFs. This tendency, however, is mitigated by the firms’ economic ties to financial institutions. Our study enhances understanding of FFs’ global entrepreneurial decision-making, contributing to FF heterogeneity research. A novel aspect of our study is examining the impact of institutional legacies on FFs – a topic less explored in family business literature. Furthermore, our findings provide insights for policymakers and regulators, emphasizing the importance of tailored policies that consider the intricate interplay between institutional origin and contemporary entrepreneurial goals in FFs.

Although the evidence is limited, examples of professional female poets who composed public songs for their communities, commissioned by wealthy families and women patrons, suggest that female performance activated the same economic dynamics as the work of male poets in relation to their patrons. Thus, women contributed to the economic life of their communities through their poetic voices, and were able to express their views on social, political and economic matters.

Regardless of method, political scientists often seek to develop arguments that can be generalized to a population of cases. But is this the only way to think about how cases speak to one another? We advocate for a new way to think about how qualitative research produces broadly applicable insights: translation. Much like linguistic translation, the goal of translation in political science is to develop ideas that are intelligible in a different context, even as the context will change how an idea or political practice is interpreted or enacted. Translation offers at least three benefits. It allows us to (1) rethink how we form and deploy concepts; (2) rethink what a generalizable argument is by carrying parts of an argument, instead of entire causal chains to other cases; and (3) rethink how we conceptualize knowledge accumulation to include an abductive process where generating theory is the primary goal.

This paper re-evaluates recent kinship studies in Neolithic Ireland through a close analysis of biomolecular and fine-grained archaeological data. It outlines the rich possibilities these datasets offer when interwoven to enhance our understanding of diverse webs of social relationships. We synthesize a range of archaeological and scientific data to form a new model of kinship and its relationship to shifting traditions of megalith building and funerary and cosmological practices. This model is put in dialogue with recently published genetic data and used to test a variety of explanations for the patterns of biological relatedness revealed using these methods. We argue that the detected genetic patterning is best interpreted as reflecting a reconfiguration of social relations after 3600 bc linked to the consolidation of emergent social and religious communities.

Survey research in the Global South has traditionally required large budgets and lengthy fieldwork. The expansion of digital connectivity presents an opportunity for researchers to engage global subject pools and study settings where in-person contact is challenging. This paper evaluates Facebook advertisements as a tool to recruit diverse survey samples in the Global South. Using Facebook’s advertising platform, we quota-sample respondents in Mexico, Kenya, and Indonesia and assess how well these samples perform on a range of survey indicators, identify sources of bias, replicate a canonical experiment, and highlight trade-offs for researchers to consider. This method can quickly and cheaply recruit respondents, but these samples tend to be more educated than corresponding national populations. Weighting ameliorates sample imbalances. This method generates comparable data to a commercial online sample for a fraction of the cost. Our analysis demonstrates the potential of Facebook advertisements to cost-effectively conduct research in diverse settings.

We investigate the effects of age and first language (L1) on the acquisition of verb morphology in L2 English by Chinese and Russian children learning English as a foreign language in EFL schools in Shanghai and Moscow. We tested children 5 years after they started their EFL classes and considered two groups in each country: one group started EFL classes at the age of 4 and was tested at the age of 9, while the other group started at 7 and was tested at 12. We assessed the production of 3SG-agreement and past tense using two elicited production tasks (TEGI). Our results show that later starters consistently outperform earlier starters. Unexpectedly, Chinese children showed higher accuracy with 3SG-agreement than their Russian counterparts. Finally, learners were more accurate with regular past tense than 3SG-agreement.

This article traces the reproduction of whiteness in Jamaica during the late nineteenth and early twentieth centuries through the lens of domestic labor. Articulated in dialogue—and at times in tension—with Britain, what it meant to be white was forged through representations and practices of domestic service and household management, shaped by the legacies of slavery and the shifting colonial relationship. Anxieties about a declining white population and attempts to rejuvenate the island's image contributed to prescriptions of domestic labor management that positioned the white creole mistress as a model of respectability and colonial modernity. Black domestic servants were repeatedly presented as the mirror through which white creole womanhood was constructed, and this article argues that these representations served to consolidate class/color hierarchies that privileged whiteness into the twentieth century. Yet mapping these discourses onto the daily interactions between mistress and maid also exposes the persistent work required to secure racialized hierarchies. Through photographs, diaries, and correspondence read alongside published oral histories, the article argues that domestic servants persistently exercised agency that disrupted and spoke back to popular depictions, demonstrating the fraught reproduction of creole whiteness at the intersections of race, class, color, gender, and colonial identity.

In this paper, we derive simple analytical bounds for solutions of $x - \ln x = y -\ln y$, and use them for estimating trajectories following Lotka–Volterra-type integrals. We show how our results give estimates for the Lambert W function as well as for trajectories of general predator–prey systems, including, for example, Rosenzweig–MacArthur equations.

A fundamental pillar of science is the estimation of the effect size of associations. However, this task is sometimes difficult and error-prone. To facilitate this process, the R package metaConvert automatically calculates and flexibly converts multiple effect size measures. It applies more than 120 formulas to convert any relevant input data into Cohen’s d, Hedges’ g, mean difference, odds ratio, risk ratio, incidence rate ratio, correlation coefficient, Fisher’s r-to-z transformed correlation coefficient, variability ratio, coefficient of variation ratio, or number needed to treat. Researchers unfamiliar with R can use this software through a browser-based graphical interface (https://metaconvert.org/). We hope this suite will help researchers in the life sciences and other disciplines estimate and convert effect sizes more easily and accurately.

The idea of a “public humanities” reflects a specific understanding not only of the humanities as a field of interest or set of disciplines but also of citizens’ needs in a democracy. This understanding was most fully articulated in the United States, where it informed the national understanding of the goals of education that emerged in the aftermath of WWII. What distinguishes the American conception of the humanities from other systems is the place of privilege accorded the activities of interpretation and judgment, and the conviction that these were most effectively inculcated through the study of literature.

Plusieurs personnes atteintes d’un cancer incurable sont des hommes, âgés de plus de 65 ans. Même si, à notre connaissance, aucune recherche ne porte sur les souffrances de ces hommes, des écrits dévoilent que ces dernières pourraient être considérables. Les réalités du vieillissement au masculin couplées à celles d’être atteint d’une maladie incurable peuvent effectivement engendrer des souffrances particulières. Cette recherche qualitative vise à mieux comprendre les souffrances vécues par les hommes âgés atteints d’un cancer incurable (HACI). Nous explorerons si et comment leurs souffrances se rapportent à des enjeux identitaires de genre et/ou aux transformations de leurs rôles. Dix-sept hommes âgés de 65 ans et plus atteints d’un cancer incurable furent rencontrés en milieu urbain (Montréal) dans le cadre d’entretiens semi-dirigés. L’analyse thématique des résultats a permis de relever des enjeux identitaires et de rôles, la nature des changements affectant l’identité et ses rôles et les enjeux de genre vécus par les HACI.

Machine learning has exhibited substantial success in the field of natural language processing (NLP). For example, large language models have empirically proven to be capable of producing text of high complexity and cohesion. However, at the same time, they are prone to inaccuracies and hallucinations. As these systems are increasingly integrated into real-world applications, ensuring their safety and reliability becomes a primary concern. There are safety critical contexts where such models must be robust to variability or attack and give guarantees over their output. Computer vision had pioneered the use of formal verification of neural networks for such scenarios and developed common verification standards and pipelines, leveraging precise formal reasoning about geometric properties of data manifolds. In contrast, NLP verification methods have only recently appeared in the literature. While presenting sophisticated algorithms in their own right, these papers have not yet crystallised into a common methodology. They are often light on the pragmatical issues of NLP verification, and the area remains fragmented. In this paper, we attempt to distil and evaluate general components of an NLP verification pipeline that emerges from the progress in the field to date. Our contributions are twofold. First, we propose a general methodology to analyse the effect of the embedding gap – a problem that refers to the discrepancy between verification of geometric subspaces, and the semantic meaning of sentences which the geometric subspaces are supposed to represent. We propose a number of practical NLP methods that can help to quantify the effects of the embedding gap. Second, we give a general method for training and verification of neural networks that leverages a more precise geometric estimation of semantic similarity of sentences in the embedding space and helps to overcome the effects of the embedding gap in practice.

Do ideologically extreme candidates enjoy fundraising advantages over more moderate candidates? Extant work documents a relationship between candidates’ positions and campaign contributions subnationally and in donor surveys, yet identification challenges have hampered investigation in the congressional context. I employ a close primaries regression discontinuity design to examine how “as-if random” nominations of extreme versus moderate House candidates influence general election contributions from individual donors and corporate political action committees (PACs) from 1980 to 2020. Results at both the nominee and contributor levels demonstrate that corporate PACs financially penalize extremists, while individual donors respond similarly to extreme and moderate candidates. These findings contribute to ongoing debates regarding the extent and nature of campaign contributors’ role in congressional polarization.

This article examines the death of Colin Roach in Stoke Newington Police Station, Hackney, in 1983, and explores the emotional politics of the campaigns that followed his death. These campaigns were focused on both determining the circumstances of Roach's death and highlighting tensions between the police and the Black community of Hackney. Using hitherto unpublished archival sources, local newspapers, and visual sources, the article documents racial politics in Hackney in the early 1980s and examines the relationship between race and policing at that time. The article argues that the experience and expression of grief and anger were critical to understanding the political problem of race and policing in London in the 1980s, to forming and mobilizing political communities, and to interrogating the power of the state. The article also argues that a critical element of the emotional economy around race in Hackney in 1983 was the indifference and lack of empathy of the police in Stoke Newington to ethnic minority communities. This lack of empathy not only illustrated the problem of race within the police force at this time but further fueled local campaigns to make the police accountable. This links the Roach case to a later turning point—the 1999 Macpherson inquiry into the murder of Stephen Lawrence, which characterized the Metropolitan Police as institutionally racist.

We investigate the role of visual attention in risky choice in a rich experimental dataset that includes eye-tracking data. We first show that attention is not reducible to individual and contextual variables, which explain only 20% of attentional variation. We then decompose attentional variation into individual average attention and trial-wise deviations of attention to capture different cognitive processes. Individual average attention varies by individual, and can proxy for individual preferences or goals (as in models of “rational inattention” or goal-directed attention). Trial-wise deviations of attention vary within subjects and depend on contextual factors (as in models of “salience” or stimulus-driven attention). We find that both types of attention predict behavior: average individual attention patterns are correlated with individual levels of loss aversion and capture part of this individual heterogeneity. Adding trial-wise deviations of attention further improves model fit. Our results show that a decomposition of attention into individual average attention and trial-wise deviations of attention can capture separable cognitive components of decision making and provides a useful tool for economists and researchers from related fields interested in decision-making and attention.