Many political science theories posit empirical relationships that are conditioned by some moderating variable, which scholars model using a multiplicative interaction term in a regression. In recent years, scholars have begun using such terms in dynamic regression models with time series data. However, the lack of guidance on adding multiplicative interactions to these workhorse models exposes problems with the consistency of the estimator, model restrictions, and interpretation. This paper provides theoretical and practical guidance to address these problems. First, we define the conditions under which scholars can ensure consistent estimates when estimating relationships conditioned by a moderating variable in dynamic models. Second, we introduce a general model that makes no theoretical assumptions about precisely how conditional relationships unfold over time. Third, we develop a flexible approach for interpreting such models. We demonstrate the advantages of this framework with simulation evidence and an empirical application.

The successful establishment of the bacterial community in the gastrointestinal tract is of crucial importance for growth, metabolic health, performance and welfare in early and later stages of ruminants. This study aimed to investigate the development of oral- and rumen-associated bacteria in pre-weaning calves and to compare them with respect to the different genetic backgrounds and the type of $\beta $-casein in the milk fed. Calves were kept in the same defined nutritional and housing environment. Saliva samples were taken at three time points up to 27 days of age before weaning via buccal swabs, and the extracted DNA was analysed using 16S rRNA gene amplicon sequencing. The development of the microbiota in the rumen and oral cavity was differentially analysed and evaluated. Age-dependent changes were observed, with a clear shift in rumen-associated bacteria over time and increasing alpha diversity, while oral-associated bacteria stabilized more rapidly at a young age, demonstrating the robustness of the analysis. Breed-specific differences were noted at the genus level, and minor effects of milk type (based on β-casein type) were detected. A significant interaction was detected between calf age and bacterial abundance, as well as between the breed and bacterial abundance, but not for milk type. The method for analysing and evaluating saliva samples provided valuable insights into the development of the microbiota in the oral cavity and the rumen under the influence of breed and the milk fed by the successful separation of oral- and rumen-associated bacteria. Due to the exclusive use of samples from the oral cavity, invasive examination methods could be largely avoided in the future and would only serve as additional controls to confirm the results of the buccal swab analyses.

The paper investigates processes and consequences of ‘philanthropic kinning’, that is the use of kinship and family idioms in constructing and maintaining personal relations between donors and recipients in philanthropy. Usual studies collapse the occurrence of kinship metaphors in philanthropy either as evidence of ‘prosociality’ (e.g. trust, care or love) or more frequently as evidence of ‘paternalism’ (power and domination of donors over recipients, and their objectification). This paper claims that introducing kinship and parenting studies into researching philanthropy would greatly refine our understanding of donor–recipient relations. In the framework of a qualitative case study of a philanthropic ‘godparenthood’ programme organised in Hungary supporting ethnic Hungarian communities in Romania, this paper looks at the roles, responsibilities and obligations various forms of philanthropic kinship offer for the participants; and relations of power unfolding in helping interactions. With such concerns, this paper complements earlier research on hybridisation of philanthropy, through its sectoral entanglements with kinship and family. Also, it contributes to research on inequalities in philanthropy, by showing how philanthropic kinning may recreate, modify or reshape donor–recipient power relations in diverse ways.

To understand a treatment’s potential impact at the individual level, it is crucial to explore whether the effect differs across patient subgroups and covariate values. Meta-analysis provides an important tool for detecting treatment–covariate interactions, as it can improve power compared to a single study. However, aggregation bias can occur when estimating individual-level treatment–covariate interactions in meta-analysis, due to trial-level confounding. This refers to when the association between the covariate and treatment effect across trials (at the aggregate level) differs from that observed within trials (at the individual level). It is, thus, recommended that heterogeneity in the treatment effect at the individual level should be disentangled from that at the trial level, ideally using an individual participant data (IPD) meta-analysis. Here, we explain this issue and provide new intuition about how trial-level confounding is impacted by differences in within-trial distributions of covariates and how this corresponds to asymmetry in subgroup-specific funnel plots in the case of categorical covariates. We then propose a sensitivity analysis to assess the robustness of interaction estimates to potential trial-level confounding. We illustrate these concepts using simulated and real data from an IPD meta-analysis of trials conducted on the TICO/ACTIV-3 platform, which assessed passive immunotherapy treatments for inpatients with COVID-19.

This comparative article examines the iterative interactions between the French conception of guerre contre-révolutionnaire and the (re-)legitimation of modern torture techniques from the late nineteenth to the early twenty-first centuries. Based on a threefold argument, and drawing on multilingual historical sources and museal artifacts, it argues that the ideological campaign against the “revolutionary war” was a specifically military-intellectual approach to dealing with real or imagined subversive enemies. This dispositif promoted torture as a method of obtaining information and intimidating victims. First, this article shows how torture and the corresponding knowledge production can be traced back to colonial Indochina. There, archaic techniques were peculiarly blended, often with other experiences and indigenous practices. Later, leading military officers believed that the resulting doctrine of counterrevolutionary warfare was successful largely because of the use of methods of torture that left no trace. This key feature facilitated the export of its techniques to other regions. Therefore, in a second step, this article shows how this intertwined knowledge system was applied to the Algerian War, where it was widely employed and exploited. Subsequently, the fear of the spread of global communism facilitated the emergence of torture as a covert science of the Cold War. Third, this essay demonstrates how leading French theorists globalized their teachings by influencing their South American counterparts through their cross-continental interactions from the 1960s onward. Since the end of the Cold War, traces of this savoir-faire have remained potent, culminating in their influence on U.S. American counterinsurgency doctrine.

This paper explores the evolution of the concept of peace in the context of a globalized and digitalized 21st century, proposing a novel vision that shifts from viewing peace as a thing or a condition, to understanding peace as dynamic and relational process that emerges through human interactions. Building on - yet also going beyond - traditional definitions of peace as something to be found through inner reflection (virtue ethics), as the product of reason, contracts and institutions (Enlightenment philosophy), and as the absence of different forms of violence (modern peace research), this paper introduces a new meso-level theory on networks, emphasizing the importance of connections, interactions and relationships in the physical and online worlds. The paper is structured around three main objectives: conceptualizing relational peace in terms of the quantity and quality of interactions, mapping these interactions into networks of peace, and examining how these networks interact with their environment, including the influence of digital transformation and artificial intelligence. By integrating insights from ethical and peace research literature, the paper makes theoretical, conceptual, and methodological contributions towards understanding peace as an emergent property of human behavior. Through this innovative approach, the paper aims to provide clarity on how peace (and violence) emerges through interactions and relations in an increasingly networked and digitalized global society, offering a foundation for future empirical research and concerted policy action in this area. It highlights the need for bridging normative and descriptive sciences to better understand and promote peace in the digital age.

Conditioning on variables affected by treatment can induce post-treatment bias when estimating causal effects. Although this suggests that researchers should measure potential moderators before administering the treatment in an experiment, doing so may also bias causal effect estimation if the covariate measurement primes respondents to react differently to the treatment. This paper formally analyzes this trade-off between post-treatment and priming biases in three experimental designs that vary when moderators are measured: pre-treatment, post-treatment, or a randomized choice between the two. We derive nonparametric bounds for interactions between the treatment and the moderator under each design and show how to use substantive assumptions to narrow these bounds. These bounds allow researchers to assess the sensitivity of their empirical findings to priming and post-treatment bias. We then apply the proposed methodology to a survey experiment on electoral messaging.

Goodman's (1979, 1981, 1985) loglinear formulation for bi-way contingency tables is extended to tables with or without missing cells and is used for exploratory purposes. A similar formulation is done for three-way tables and generalizations of correspondence analysis are deduced. A generalized version of Goodman's algorithm, based on Newton's elementary unidimensional method is used to estimate the scores in all cases.

Classic item response models assume that all items with the same difficulty have the same response probability among all respondents with the same ability. These assumptions, however, may very well be violated in practice, and it is not straightforward to assess whether these assumptions are violated, because neither the abilities of respondents nor the difficulties of items are observed. An example is an educational assessment where unobserved heterogeneity is present, arising from unobserved variables such as cultural background and upbringing of students, the quality of mentorship and other forms of emotional and professional support received by students, and other unobserved variables that may affect response probabilities. To address such violations of assumptions, we introduce a novel latent space model which assumes that both items and respondents are embedded in an unobserved metric space, with the probability of a correct response decreasing as a function of the distance between the respondent’s and the item’s position in the latent space. The resulting latent space approach provides an interaction map that represents interactions of respondents and items, and helps derive insightful diagnostic information on items as well as respondents. In practice, such interaction maps enable teachers to detect students from underrepresented groups who need more support than other students. We provide empirical evidence to demonstrate the usefulness of the proposed latent space approach, along with simulation results.