While live event experiences have become increasingly mediatized, the prevalence of ephemeral content and diverse forms of (semi)private communication in social media platforms have complicated the study of these mediatized experiences as an outsider. This article proposes an ethnographic approach to studying mediatized event experiences from the inside, carrying out participatory fieldwork in online and offline festival environments. I argue that this approach both stimulates ethical research behavior and provides unique insights into mediatized practices. To develop this argument, I apply the proposed methodology to examine how festival-goers perceive differences between public and private, permanent and ephemeral when sharing their live event experiences through social media platforms. Drawing on a substantial dataset containing online and offline participant observations, media diaries, and (short in situ and longer in-depth) interviews with 379 event-goers, this article demonstrates the value of an ethnographic approach for creating thick descriptions of mediatized behavior in digital platforms.
While the original definition of replacement focuses on the replacement of the use of animals in science, a more contemporary definition focuses on accelerating the development and use of predictive and robust models, based on the latest science and technologies, to address scientific questions without the use of animals. The transition to animal free innovation is on the political agenda in and outside the European Union. The Beyond Animal Testing Index (BATI) is a benchmarking instrument designed to provide insight into the activities and contributions of research institutes to the transition to animal free innovation. The BATI allows participating organizations to learn from each other and stimulates continuous improvement. The BATI was modelled after the Access to Medicine Index, which benchmarks pharmaceutical companies on their efforts to make medicines widely available in developing countries. A prototype of the BATI was field-tested with three Dutch academic medical centers and two universities in 2020-2021. The field test demonstrated the usability and effectiveness of the BATI as a benchmarking tool. Analyses were performed across five different domains. The participating institutes concluded that the BATI served as an internal as well as an external stimulus to share, learn, and improve institutional strategies towards the transition to animal free innovation. The BATI also identified gaps in the development and implementation of 3R technologies. Hence, the BATI might be a suitable instrument for monitoring the effectiveness of policies. BATI version 1.0 is ready to be used for benchmarking at a larger scale.
Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention towards the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the circulation and contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by networked publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images is limited. However, combining automated analyses of images - broken down by their compositional elements - with repurposing platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This paper explores the capacities of platform data - hashtag modularity and retweet counts - to complement the automated assessment of social media images; doing justice to both the visual elements of an image and the contextual elements encoded by networked publics that co-create meaning.
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