Browsing by Author "Rijmer, Sylvia"
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- Being a repertoire dancer: an eclectic process of embodiment from studio to stagePublication . Rijmer, Sylvia; Instituto Politécnico de Lisboa - Escola Superior de DançaABSTRACT - Versatility and an extensive embodied repertoire are essential attributes for Contemporary dancers in today's professional landscape. The ability to engage with diverse choreographic processes which are often characterized by collaboration and co-creation,demands a broad skill set and adaptability. This paper presents an empirical and autobiographical exploration of the process of becoming and working as a Contemporary dancer across multiple dance institutions and companies. Specifically, it examines two contemporary choreographic works and their impact on the subjective experience of performing as a repertoire dancer with the now-extinct Ballet Gulbenkian. Through an analysis of two restagings from the company’s 2003/2004 season; Jiří Kylián’s Falling Angels and Marie Chouinard’s Le Sacre du Printemps, this study investigates these works as comparative and subjective practices within an eclectic dance repertoire. By reflecting on these experiences, the paper aims to contribute to discussions on the evolving role of Contemporary dancers and the demands of professional repertory work in an increasingly interdisciplinary field.
- Generative AI, decision-making, and collaborative choreography: how LSTM networks mirror human creativityPublication . Sevivas, Cláudia; Rijmer, Sylvia; Evola, Vito; Grund, Mathias; Scherffig, LasseABSTRACT - This study explores the potential of generative AI, specifically Long Short-Term Memory (LSTM) networks, to advance collaborative choreographic composition within the framework of the Body Logic (BL) Method—a choreographic approach grounded incognitive science designed to challenge inherited habits and practices in contemporary dance. Through five cognitive tasks that emphasize different movement types and their qualities, we investigate how LSTM networks recognize established movement patterns and innovate by combining them in novel ways, mirroring the processes of human creativity. Furthermore, we examine how LSTM-generated sequences, derived from learned data, convey expressive qualities through a variety of movements. The AIgenerated movements closely follow the original movement trajectory but exhibit minor deviations attributable to the LSTM model's inherent prediction uncertainty. These variations illustrate the model's capability to introduce fresh elements while maintaining learned patterns, akin to human creativity. This research contributes novel perspectives on how technology can enrich artistic expression and challenge habitual decision-making in dance.