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Millions of nonprofits compete for a share of the billions of dollars donated to charity each year. Yet how donors select which charities to support remains relatively understudied. Social norms influence whether people give to charity at all, but no research has yet considered whether norms also communicate information about which causes group members typically support. To address this important question, we surveyed 1,735 people from 117 countries to understand whether they identified normative causes typically supported by their social groups. We found different normative giving profiles for men, women, older people, younger people, conservatives, progressives, religious, and nonreligious people, with varying degrees of consensus within each social group. Results demonstrate empirically—and for the first time—that social identities contain normative content about which charitable causes group members typically support. Some causes were relatively universally approved of or avoided. Results can inform nonprofit fundraising strategy around segmentation and targeting. [ABSTRACT FROM AUTHOR]

Despite being a risky and controversial business model, little research has investigated personal characteristics that increase attraction to multi‐level marketing business opportunities like Amway and Herbalife. A two‐wave survey (Ntotal = 1503) revealed that attraction to multi‐level marketing opportunities was associated with stronger extrinsic life goals (for becoming rich, famous, and attractive), belief systems related to spirituality and thought‐action fusion (i.e., the belief that thoughts alone create reality), and a cognitive decision‐making style that emphasizes intuitive over rational thinking. Intrinsic psychological needs (e.g., for control and meaning) showed the least consistent associations. Based on these findings, we provide guidance for consumer advocates and policy makers to consider individual psychological factors in education campaigns to improve decision making and participant wellbeing. [ABSTRACT FROM AUTHOR]

This study correlated self-report and trace data measures of political incivility. Specifically, we asked respondents to provide estimates of the degree to which they engage in uncivil political communication online. These estimates were then compared to computational measures of uncivil social media discussion behavior. The results indicated that those who self-disclose uncivil online behavior also tend to generate content on social media that is uncivil as identified by Google's Perspective application programming interface. Taken as a whole, this work suggests that combining self-report and behavioral trace data may be a fruitful means of developing multimethod measures of complex communication behaviors. [ABSTRACT FROM AUTHOR]

Dropout is an important factor that may compromise the validity of findings from randomized controlled trials (RCTs) of dialectical behaviour therapy (DBT). We conducted a targeted meta-analytic review of dropout from RCTs of DBT, with the aims of (1) calculating average rates of dropout from DBT; (2) investigating factors that moderate dropout; (3) examining whether dropout rates from DBT differ to control interventions; (4) synthesising reasons for dropout. Forty RCTs of DBT met full inclusion criteria. The weighted mean dropout rate was 28.0% (95% CI = 23.6, 32.9). Dropout rates were not related to target disorder, dropout definition, delivery format, therapist experience, and therapist adherence. Unexpectedly, dropout rates were significantly higher in trials that offered telephone coaching and utilized a therapist consultation team. DBT dropout rates did not significantly differ to dropout rates from control interventions. Few trials reported reasons for dropout, and there was little consistency in the reported reasons. Findings suggest that over one in four patients drop out from DBT in RCTs. This review highlights the urgency for future trials to explicitly report detail pertaining to patient dropout, as this may assist in the development of strategies designed to prevent future dropouts in RCTs of DBT. [ABSTRACT FROM AUTHOR]

We introduce the Constructive Comments Corpus (C3), comprised of 12,000 annotated news comments, intended to help build new tools for online communities to improve the quality of their discussions. We define constructive comments as high-quality comments that make a contribution to the conversation. We explain the crowd worker annotation scheme and de ne a taxonomy of subcharacteristics of constructiveness. The quality of the annotation scheme and the resulting dataset is evaluated using measurements of inter-annotator agreement, expert assessment of a sample, and by the constructiveness sub-characteristics, which we show provide a proxy for the general constructiveness concept. We provide models for constructiveness trained on C3 using both feature-based and a variety of deep learning approaches and demonstrate, through domain adaptation experiments, that these models capture general rather than topic- or domain-specific characteristics of constructiveness. We also examine the role that length plays in our models, as comment length could be easily gamed if models depend heavily upon this feature. By examining the errors made by each model and their distribution by length, we show that the best performing models are effective independently of comment length. The constructiveness corpus and our experiments pave the way for a moderation tool focused on promoting comments that make a meaningful contribution, rather than only filtering out undesirable content. [ABSTRACT FROM AUTHOR]

Internet censors seek ways to identify and block internet access to information they deem objectionable. Increasingly, censors deploy advanced networking tools such as deep-packet inspection (DPI) to identify such connections. In response, activists and academic researchers have developed and deployed network traffic obfuscation mechanisms. These apply specialized cryptographic tools to attempt to hide from DPI the true nature and content of connections. This survey offers an overview of network traffic obfuscation and its role in circumventing Internet censorship. [ABSTRACT FROM PUBLISHER]

User posts whose perceived toxicity depends on conversational context are rare in current toxicity detection datasets. Hence, toxicity detectors trained on existing datasets will also tend to disregard context, making the detection of context-sensitive toxicity harder when it does occur. We construct and publicly release a dataset of 10,000 posts with two kinds of toxicity labels: (i) annotators considered each post with the previous one as context; and (ii) annotators had no additional context. Based on this, we introduce a new task, context sensitivity estimation, which aims to identify posts whose perceived toxicity changes if the context (previous post) is also considered. We then evaluate machine learning systems on this task, showing that classifiers of practical quality can be developed, and we show that data augmentation with knowledge distillation can improve performance further. Such systems could be used to enhance toxicity detection datasets with more context-dependent posts, or to suggest when moderators should consider parent posts, which often may be unnecessary and may otherwise introduce significant additional costs. [ABSTRACT FROM AUTHOR]

The Theory Mine novelty gift company sells the rights to name novel mathematical theorems. The technology is made up of a pipeline of four computer systems that generates recursive theories, then speculates conjectures in those theories and proves the conjectures. All stages of the theorem discovery and proof processes are completely automatic. The process guarantees large numbers of sound, novel theorems of some intrinsic merit. [ABSTRACT FROM PUBLISHER]

String diagrams are a powerful tool for reasoning about physical processes, logic circuits, tensor networks and many other compositional structures. The distinguishing feature of these diagrams is that edges need not be connected to vertices at both ends, and these unconnected ends can be interpreted as the inputs and outputs of a diagram. In this paper, we give a concrete construction for string diagrams using a special kind of typed graph called an open-graph. While the category of open-graphs is not itself adhesive, we introduce the notion of a selective adhesive functor, and show that such a functor embeds the category of open-graphs into the ambient adhesive category of typed graphs. Using this functor, the category of open-graphs inherits ‘enough adhesivity’ from the category of typed graphs to perform double-pushout (DPO) graph rewriting. A salient feature of our theory is that it ensures rewrite systems are ‘type safe’ in the sense that rewriting respects the inputs and outputs. This formalism lets us safely encode the interesting structure of a computational model, such as evaluation dynamics, with succinct, explicit rewrite rules, while the graphical representation absorbs many of the tedious details. Although topological formalisms exist for string diagrams, our construction is discrete and finitary, and enjoys decidable algorithms for composition and rewriting. We also show how open-graphs can be parameterised by graphical signatures, which are similar to the monoidal signatures of Joyal and Street, and define types for vertices in the diagrammatic language and constraints on how they can be connected. Using typed open-graphs, we can construct free symmetric monoidal categories, PROPs and more general monoidal theories. Thus, open-graphs give us a tool for mechanised reasoning in monoidal categories. [ABSTRACT FROM AUTHOR]

Abstract: We present an approach to mathematical assistants which uses readable, executable proof scripts as the central language for interaction. We examine an implementation that combines the Isar language, the Isabelle theorem prover and the IsaPlanner proof planner. We argue that this synergy provides a flexible environment for the exploration, certification, and presentation of mathematical proof. [Copyright &y& Elsevier]

Evaluating large language models (LLMs) presents unique challenges. While automatic side-by-side evaluation, also known as LLM-as-a-judge, has become a promising solution, model developers and researchers face difficulties with scalability and interpretability when analyzing these evaluation outcomes. To address these challenges, we introduce LLM Comparator, a new visual analytics tool designed for side-by-side evaluations of LLMs. This tool provides analytical workflows that help users understand when and why one LLM outperforms or underperforms another, and how their responses differ. Through close collaboration with practitioners developing LLMs at Google, we have iteratively designed, developed, and refined the tool. Qualitative feedback from these users highlights that the tool facilitates in-depth analysis of individual examples while enabling users to visually overview and flexibly slice data. This empowers users to identify undesirable patterns, formulate hypotheses about model behavior, and gain insights for model improvement. LLM Comparator has been integrated into Google's LLM evaluation platforms and open-sourced.

We explored the psychology of those who believe in manifestation: the ability to cosmically attract success in life through positive self-talk, visualization, and symbolic actions (e.g., acting as if something is true). In three studies (collective N = 1,023), we developed a reliable and valid measure-the Manifestation Scale-and found over one third of participants endorsed manifestation beliefs. Those who scored higher on the scale perceived themselves as more successful, had stronger aspirations for success, and believed they were more likely to achieve future success. They were also more likely to be drawn to risky investments, have experienced bankruptcy, and to believe they could achieve an unlikely level of success more quickly. We discuss the potential positives and negatives of this belief system in the context of growing public desire for success and an industry that capitalizes on these desires., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.