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on Cultural Economics |
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Issue of 2024‒05‒27
six papers chosen by Roberto Zanola, Università degli Studi del Piemonte Orientale |
| By: | Salvaggio, Salvino A. PhD |
| Abstract: | This brief note explores the debate surrounding the use of dual leadership models, where an executive director and an artistic director both report to the board, in the management of arts and cultural institutions, including orchestras and operas. It highlights that while the dual leadership model offers benefits such as enhanced creativity, improved decision-making, and increased organisational adaptability, it also introduces potential challenges like conflict, divergent objectives, and increased complexity in leadership dynamics. The note highlights the need for a balanced consideration of the advantages and drawbacks of both single and dual leadership models to support the blend of artistic excellence and managerial efficacy in successful arts and cultural organisations. |
| Date: | 2024–04–21 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:gvqhb&r=cul |
| By: | Salvaggio, Salvino A. |
| Abstract: | This brief note explores the varied funding models for orchestras worldwide, illustrating how historical, cultural, political, and economic factors shape their financial strategies. Orchestras in Europe often benefit from substantial government support, viewing cultural pursuits as public goods, whereas those in the United States predominantly rely on private funding due to limited public subsidies. A hybrid model is also discussed, prevalent in the UK, Asia, and Australia, which combines governmental support with private sources to ensure financial stability and uphold the cultural significance of orchestral music. The note emphasises the need for skilled management across these diverse funding environments to sustain long-term viability and achieve artistic goals. |
| Date: | 2024–04–28 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:awdpv&r=cul |
| By: | D'Amico, Enrique; Del Giorgio Solfa, Federico Ph.D. (National University of La Plata) |
| Abstract: | The paradigm shift has revealed a need among industrial designers to have entrepreneurial capabilities. Being the most effective modality to exercise discipline today. The implementation of this approach requires reviewing the discursive scaffolding that the design has been using to set immovable high standards of complexity and quality, reducing the field of professional action and limiting the intervention alternatives. This article considers the dynamics and trends in people's lifestyles, explores possible strategies and specific content for the entrepreneurial training required by design. |
| Date: | 2024–04–06 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:mcet3&r=cul |
| By: | Fleming, Kate |
| Keywords: | Arts and Humanities, Hearst, Near Eastern Studies, 150w, UC RegentsBerkeley |
| Date: | 2024–05–03 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:cshedu:qt4322s33g&r=cul |
| By: | Ranjan, Pramod (Assam University) |
| Abstract: | This Hindi Paper discusses the future of Hindi language models in the context of artificial intelligence (AI). It highlights the rapid advancements in AI, particularly during the COVID-19 pandemic, which has significantly impacted various professions, including writers, poets, musicians, artists, educators, and software coders. The article delves into the capabilities and limitations of AI in writing and its implications for the Hindi language. It also examines the role of large language models (LLMs) in enabling machines to write and understand human-like text, and the ethical considerations surrounding AI-generated content. The piece emphasizes the importance of addressing biases in AI models to ensure they reflect a fair and accurate representation of society. |
| Date: | 2024–04–16 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:qprfm&r=cul |
| By: | Jiachen T. Wang; Zhun Deng; Hiroaki Chiba-Okabe; Boaz Barak; Weijie J. Su |
| Abstract: | Generative artificial intelligence (AI) systems are trained on large data corpora to generate new pieces of text, images, videos, and other media. There is growing concern that such systems may infringe on the copyright interests of training data contributors. To address the copyright challenges of generative AI, we propose a framework that compensates copyright owners proportionally to their contributions to the creation of AI-generated content. The metric for contributions is quantitatively determined by leveraging the probabilistic nature of modern generative AI models and using techniques from cooperative game theory in economics. This framework enables a platform where AI developers benefit from access to high-quality training data, thus improving model performance. Meanwhile, copyright owners receive fair compensation, driving the continued provision of relevant data for generative model training. Experiments demonstrate that our framework successfully identifies the most relevant data sources used in artwork generation, ensuring a fair and interpretable distribution of revenues among copyright owners. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.13964&r=cul |