Comparing international coverage of 9/11 : towards an interdisciplinary explanation of the construction of news

This article presents an interdisciplinary model attempting to explain how news is constructed by relying on the contributions of different fields of study: News Sociology, Political Communications, International Communications, International Relations. It is a first step towards developing a holistic theoretical approach to what shapes the news, which bridges current micro to macro approaches. More precisely the model explains news variation across different media organization and countries by focusing on the different way the sense of newsworthiness of journalists is affected by three main variables: national interest, national journalistic culture, and editorial policy of each media organization. The model is developed on the basis of an investigation into what shaped the media coverage of 9/11 in eight elite newspapers across the US, France, Italy and Pakistan

The soft power of popular cinema: the case of India

Among BRICS nations, India has the most developed and globalised film industry, and the Indian government as well as corporations are increasingly deploying the power of Bollywood in their international interactions. India’s soft power, arising from its cultural and civilizational influence outside its territorial boundaries, has a long history. Focusing on contemporary India’s thriving Hindi film industry, this article suggests that the globalisation of the country’s popular cinema, aided by a large diaspora, has created possibilities of promoting India’s public diplomacy. It examines the global imprint of this cinema as an instrument of soft power

FAIR AI Models in High Energy Physics

The findable, accessible, interoperable, and reusable (FAIR) data principles serve as a framework for examining, evaluating, and improving data sharing to advance scientific endeavors. There is an emerging trend to adapt these principles for machine learning models—algorithms that learn from data without specific coding—and, more generally, AI models, due to AI’s swiftly growing impact on scientific and engineering sectors. In this paper, we propose a practical definition of the FAIR principles for AI models and provide a template program for their adoption. We exemplify this strategy with an implementation from high-energy physics, where a graph neural network is employed to detect Higgs bosons decaying into two bottom quarks

FAIR AI models in high energy physics

The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability