Search results

"The law and policy applicable to the not-for-profit sector is of growing importance around the world. In this book, legal experts address fundamental questions about not-for-profit law from a range of theoretical and comparative perspectives. The essays provide scholarly analysis of not-for-profit law, organised around four themes: (1) Politics, in the broader sense of living as a community, and the narrower sense of political power; (2) Charity, how it is defined and changes in its meaning over time; (3) Taxation, including the rationale for government support of the sector through the tax system; (4) Regulation, which is of increasing significance as governments establish increasingly complex forms of regulation of not-for-profit activity. The fundamental aim of the book is to deepen our understanding of not-for-profit law and of the rationales and modes of government support for the not-for-profit sector"--

Abstract
Recent artificial intelligence (AI) tools have demonstrated the ability to produce outputs traditionally considered creative. One such system is text-to-image generative AI (e.g. Midjourney, Stable Diffusion, DALL-E), which automates humans' artistic execution to generate digital artworks. Utilizing a dataset of over 4 million artworks from more than 50,000 unique users, our research shows that over time, text-to-image AI significantly enhances human creative productivity by 25% and increases the value as measured by the likelihood of receiving a favorite per view by 50%. While peak artwork Content Novelty, defined as focal subject matter and relations, increases over time, average Content Novelty declines, suggesting an expanding but inefficient idea space. Additionally, there is a consistent reduction in both peak and average Visual Novelty, captured by pixel-level stylistic elements. Importantly, AI-assisted artists who can successfully explore more novel ideas, regardless of their prior originality, may produce artworks that their peers evaluate more favorably. Lastly, AI adoption decreased value capture (favorites earned) concentration among adopters. The results suggest that ideation and filtering are likely necessary skills in the text-to-image process, thus giving rise to "generative synesthesia"—the harmonious blending of human exploration and AI exploitation to discover new creative workflows.

"We begin with two bibliographical observations. First, scholarly interest in trust is no recent phenomenon, but lately there has been a flowering of academic literature studying numerous dimensions of trust from the standpoints of philosophy, economics, sociology and psychology. The depth and richness of this literature is impressive but hardly surprising, given that trust itself is a notoriously complex, elusive, and fact-specific phenomenon. Secondly, scholarly interest in the fiduciary principle that plays such a central role in common law legal systems with a tradition of equity was scarce until the late twentieth century. However, that situation has most definitely changed (for the better), and we now enjoy an abundance of scholarship exploring the fiduciary principle in private law. Moreover, there is a growing body of work exploring ideas of fiduciary government and international law. Scholars are puzzling over fiduciaries and trust as never before. Given this, one might expect to see a flourishing of academic interest in the relation of fiduciaries and trust, especially as it is often assumed or asserted that such a relation exists and that it has descriptive or normative significance for fiduciary law and practice. Yet this is not the case. Systematic analysis of fiduciaries and trust is rare. The aim of this volume is to help fill this gap. Our contributors explore the interactions of fiduciary law and trust, drawing on literatures on trust that have been generated in a variety of disciplines. They do so with an eye to the full scope of extension claimed for the fiduciary principle, from its heartland in private law, to its frontiers in public law and government more broadly. Overall, the volume advances an integrated and wide-ranging understanding of the relation of fiduciaries and trust that illuminates key legal and political problems, and challenges and deepens our understanding of fiduciaries and trust themselves."

"Adopting a critical multijurisdictional approach to charity law, this thought-provoking book provides a comprehensive analysis of the challenges facing charitable organisations. Exploring the contrasting approaches to charity governance and regulation in both common law and civil law jurisdictions, the book imparts practical guidance for a vast array of stakeholders in the charity law field. Rosemary Teele Langford brings together unique perspectives from a diverse range of eminent scholars across the globe, presenting invaluable insights on charity governance and regulation from multiple jurisdictions such as England and Wales, Australia, Germany, Korea and Japan (to name a few). Using a comparative methodology, the book delves deeply into a number of important issues relating to charity governance and regulation that transcend national boundaries. Key themes include the nature of charity accountability, the importance of trust and transparency, the refocusing of charity governance duties, the evaluation of charity law frameworks and regulatory approaches, and a forward-thinking appraisal of legislative reforms. Perceptive and progressive in approach, this book will inspire future scholarly discourse and will prove invaluable to academics, practitioners, regulators, charity leaders, policymakers and students across a number of jurisdictions"-- Provided by publisher on back cover

Purpose: To determine whether baseline fruit and vegetable (FV) intake or other predictors are associated with response to food vouchers (change in FV intake) among low-income adults. Design: Secondary analysis of a randomized, 2 x 2-factorial, community-based trial. Setting: San Francisco, California. Subjects: 359 low-income adults aged ≥21 years old. Intervention: Participants were mailed $20 of food vouchers monthly for 6 months, and randomized to 1 of 4 arms according to: eligible foods (FV only or any foods) and redemption schedule (weekly or monthly). Measures: Change in FV intake measured in cup equivalents between baseline and month 6 of the trial, based on 24-hour dietary recalls. Analysis: Quantile multivariate regressions were employed to measure associations between key predictors and change in FV intake across study arms. Results: FV-only weekly vouchers were associated with increased FV intake at the 25th percentile (0.24 cups/day, p = 0.048) and 50th percentile (0.37 cups/day, p = 0.02) of the distribution, but not at lower and higher quantiles. Response to the vouchers diminished 0.10 cups/day for each additional household member ( p = 0.02). Conclusion: Response to food vouchers varied along the FV intake distribution, pointing to some more responsive groups and others potentially needing additional support to increase FV intake. Larger households likely need vouchers of higher dollar value to result in similar changes in dietary intake as that observed in smaller households.

Abstract
The emergence of large language models (LLMs) has sparked considerable interest in their potential application in psychological research, mainly as a model of the human psyche or as a general text-analysis tool. However, the trend of using LLMs without sufficient attention to their limitations and risks, which we rhetorically refer to as "GPTology", can be detrimental given the easy access to models such as ChatGPT. Beyond existing general guidelines, we investigate the current limitations, ethical implications, and potential of LLMs specifically for psychological research, and show their concrete impact in various empirical studies. Our results highlight the importance of recognizing global psychological diversity, cautioning against treating LLMs (especially in zero-shot settings) as universal solutions for text analysis, and developing transparent, open methods to address LLMs' opaque nature for reliable, reproducible, and robust inference from AI-generated data. Acknowledging LLMs' utility for task automation, such as text annotation, or to expand our understanding of human psychology, we argue for diversifying human samples and expanding psychology's methodological toolbox to promote an inclusive, generalizable science, countering homogenization, and over-reliance on LLMs.