This article analyzes the privatization of Telecom Italia as part of a broader historical evaluation of public ownership in a high-technology strategic sector. Drawing on archival material from Italian state-owned enterprises as well as national and European institutions, it traces the reorganization of the telecommunications conglomerate in the 1980s and 1990s, the European regulatory framework in which it unfolded, and the company’s post-divestment trajectory, thereby questioning the inevitability of privatization. The evidence shows that, before privatization, Telecom Italia had already become a profitable, technologically advanced, and internationally competitive firm under public ownership. The decision to divest reflected domestic political and fiscal priorities, as the European liberalization of the telecommunications market reshaped the rules while leaving member states a margin of maneuver. By comparing the public and privatized phases of the company’s development, the article contributes to the historical reassessment of state-owned enterprises and challenges the assumption that ownership change was functional to securing efficiency and competitiveness in the late twentieth-century telecommunications industry.

Currently, no globally agreed mid-upper arm circumference (MUAC) cut-offs exist to identify obesity in pregnant women. This study aimed to validate MUAC as an alternative to body mass index (BMI) for detecting maternal overweight/obesity and establish optimal cut-offs. An ambispective cohort study was conducted in six Eastern Ethiopian hospitals, involving 2,502 singleton pregnant women enrolled before 16 weeks of gestation. Maternal weight and MUAC were measured twice (early and late pregnancy). Using early-pregnancy BMI as the gold standard, MUAC’s diagnostic accuracy was evaluated via linear regression and receiver operating characteristic (ROC) curves. Optimal MUAC cut-off points were determined using Youden’s index, and diagnostics were calculated to evaluate MUAC performance. A significant positive correlation was observed between BMI and MUAC (early pregnancy: r = 0.69; late pregnancy: r = 0.72). Optimal MUAC cut-offs for overweight were 25.95 cm (early) and 27.05 cm (late); for obesity, 27.75 cm (early) and 28.85 cm (late), with high sensitivity, specificity, and predictive values. Regression equations of BMI during early pregnancy = 1.09 + 0.89 × MUAC (95% CI: 0.85–0.93; p < 0.001) and late pregnancy BMI = −1.69 + 0.94 × MUAC (95% CI: 0.90–0.98; p < 0.001). The MUAC values > 26 cm for maternal overweight and > 28 for maternal obesity demonstrated good discriminatory performance and may serve as practical screening thresholds in resource limited settings. Broader validation across diverse populations could facilitate the integration of MUAC into routine antenatal screening program, strengthening early identification and management of maternal overweight/obesity.

Generative artificial intelligence (GenAI) enables foreign language learners to extend their learning beyond formal instruction and develop their autonomy. However, research has not adequately examined how learners regulate their learning with GenAI or how their GenAI literacy and multiple types of interactions influence their self-regulated learning (SRL) in GenAI-supported informal digital language learning settings. We address this gap by analyzing data from 343 Chinese university foreign language learners through partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN). PLS-SEM showed that awareness and evaluation significantly predicted GenAI-supported SRL (GenAI-SRL), whereas usage and ethics did not. Student–student, student–teacher, and student–GenAI interactions emerged as facilitators of GenAI-SRL. These three interaction types also significantly influenced most GenAI literacy dimensions, with three of them predicting awareness, usage, and evaluation, while only student–student and student–GenAI interactions significantly predicted ethics. Mediation analysis demonstrated that awareness and evaluation partially mediated the effects of student–student and student–teacher interactions on GenAI-SRL. The mediating pathways through student–GenAI interaction were not significant. ANN models identified student–student interaction as the strongest predictor of GenAI-SRL. These findings inform GenAI literacy development and the design of systems to support GenAI-SRL in informal learning contexts.

Past research on political activism has largely emphasized the role of intrapersonal developments. One prominent example is the so-called ‘conveyor belt’ metaphor, suggesting that political activism typically begins with normative political activism (NPA) – such as signing petitions – but can eventually escalate into nonnormative political activism (NNPA), eg violent protests. However, theoretical developments in the realm of socially and/or ethically aversive personality strongly emphasize the role of interpersonal differences, ie that different individuals, in general, are inclined toward NPA vs. NNPA. We herein test this conjecture and investigate whether the socially aversive personality can dissociate the tendency for different individuals to engage in NPA or NNPA. To do so, we conducted four studies (total N = 4,737) across two languages (English and German), administering a measure of the dark core of personality (D) and two different measures of NPA vs. NNPA, respectively. Results consistently indicated that individuals higher in D were more inclined toward NNPA and concurrently less inclined toward NPA – and vice versa for those low in D. Stated simply, aversive personality dissociates between NPA and NNPA, showing that different individuals are more likely to engage in one vs. the other. Thus, the present work adds to the growing evidence signifying the importance of personality for political behavior, in particular by offering a single personality trait which can differentiate the tendency to engage in NPA vs. NNPA.

This article analyzes the development paradox posed by the energy transition. It examines the interplay between Zimbabwe’s lithium potential and the dominant role played by China in the sector. Zimbabwe’s attempts to become a leading supplier of lithium through a series of policy interventions coincides with China’s growing electric vehicle and battery markets that fuel the mineral’s demand. However, power asymmetries in the relationship—as well as domestic constraints posed by weak state capacity, political economy challenges, and social costs of mining—limit the Zimbabwe government’s ability to translate its mineral wealth into developmental outcomes. This illustrates the broader dilemma of the global energy transition for producer countries: critical minerals do not automatically yield development; instead, outcomes depend on enhancing domestic agency and regulating external influence.

This paper examines why voters of radical parties do not experience an increase in satisfaction with democracy (SWD) after their electoral entry. Drawing on evidence that radical party voters are among the most affectively polarized, we argue that negative affect towards the out-group winner may outweigh the utility gains from in-group results, thus counterbalancing any entry-driven SWD boost. To evaluate our theory, we combine observational, experimental, and qualitative evidence from two studies of Éric Zemmour voters, the radical right candidate who disrupted the 2022 French elections. Our findings confirm that Zemmour voters did not experience the expected boost in SWD after their first election and provide evidence of a negative affective response to the out-group (Macron) win as the driving mechanism. The qualitative analysis further supports a causal path from negative feelings towards the winner to questioning the democratic system. Contrary to representation theories, our paper suggests that the institutional inclusion of marginalized political groups may not correct democratic dissatisfaction in highly polarized contexts.

Research on individual differences in social network analysis has primarily focused on how personality traits influence individuals’ positions and behaviors within social structures. However, attachment research has consistently shown that attachment styles strongly affect how people form and maintain their interpersonal relationships. This study examined how attachment styles relate to different types of individual personal networks. A classification of personal networks was developed based on structural indicators of cohesion, transitivity, and subgroup configuration, among other measures. Density, fragmentation, and centralization emerged as the most discriminant metrics for clustering solutions. Results indicated that attachment styles have greater explanatory power than the Big Five model in accounting for distinct relational configurations. Specifically, secure attachment was associated with dense, noncentralized, and supportive personal networks, whereas avoidant attachment and openness to experience were linked to fragmented, modular, and less supportive networks. Sociodemographic variables showed the highest predictive value for the type of personal network.

The article examines efforts by the Combined Joint Task Force—Operation Inherent Resolve (CJTF-OIR) to enumerate the harm its forces inflicted on Syrian and Iraqi civilians between 2014 and 2018. Drawing on more than 1,300 declassified civilian harm assessments, this article examines the rationale behind the decision to count civilian casualties, the policies that governed how civilian casualties were counted, and what CJTF-OIR officials did with the data collected. Although accurate counts are critical to ethical debates, we show that, on their own, these counts are insufficient when it comes to recognizing the harm inflicted upon civilians and holding militaries accountable. We trace coalition metrics back to concerns about consequence management, showing how martial considerations about optimizing violence—rather than moral concerns about constraining violence—governed the enumerative enterprise. We argue that the way the coalition previously counted civilian casualties erased certain harms from view, including the indirect, cumulative, and reverberating violence that civilians suffered during this conflict. Furthermore, we contend that these numerical indicators tell us little about how civilians experience these harms, a lack of which can become an impediment to ethical consideration. Finally, we contrast the coalition count with Airwars figures to reveal both the numerical discrepancies between the different counts and differences in how these figures have been used. We contend that counting casualties is critical, but also complicated, contestable, and—at times—too constrictive.

We develop a novel asymptotic theory for local polynomial extremum estimators of time-varying parameters in a broad class of nonlinear time-series models, including discrete-valued ones. We show that the proposed estimators are asymptotically normally distributed under weak conditions. We also provide a precise characterization of the leading bias term due to smoothing. We demonstrate the usefulness of our general results by establishing primitive conditions for local (quasi-)maximum-likelihood estimators of time-varying models threshold autoregressions, ARCH models and Poisson autoregressions with exogenous covariates, to be normally distributed in large samples and characterize their leading biases. An empirical study of U.S. corporate default counts demonstrates the applicability of the proposed local linear estimator for Poisson autoregressions, shedding new light on the dynamic properties of U.S. corporate defaults.

This article examines reactions to the Christian nationalist imagery and rhetoric employed during the January 6th, 2021 riot at the U.S. Capitol. We find evidence of both religious and partisan backlash among people who recalled evidence of Christian nationalism displayed by the January 6th rioters. Among Independents and Republicans who recalled religious symbols on January 6th, their overall religiosity declined. We also find that January 6th led not only to religious backlash but also partisan backlash—generally lessening favorability toward Donald Trump and Republicans and even leading Republicans to change their party identification.

The growing body of research on the effects of computerized dynamic assessment (C-DA) on second language (L2) learning underscores the need for a comprehensive research synthesis to identify future research directions and inform the application of C-DA in L2 educational contexts. This meta-analysis employed a three-level modeling approach to examine the effectiveness of C-DA in improving L2 learners’ performance. It synthesized 27 effect sizes from cake format designs, in which mediation is embedded within the test sequence, and 24 effect sizes from sandwich format designs, where mediation is delivered between a pretest and a posttest, across 35 studies published between 2000 and May 27, 2025. This study also investigated the key variables that moderate C-DA effectiveness. Findings reveal large, significant positive effects of both the cake and sandwich formats on L2 performance improvement (cake format: g = 2.120, p < .001; sandwich format: g = 1.676, p < .001), with the cake format tending to yield larger effect sizes. This may be because the cake format captures gains during mediation, whereas the sandwich format reflects post-mediation outcomes. Moderator analyses show that the number of items, test content, and learners’ first language affect C-DA effectiveness in promoting L2 performance. Drawing on the synthesized findings, this study contributes to theoretical, methodological, and technological understandings of C-DA and offers suggestions for future research in this domain.