A Critical Analysis of Cultural Representation in Select Beginning Band Method Books PDF Download
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Author: Daniel Taylor Publisher: ISBN: Category : Band music Languages : en Pages : 99
Book Description
Using a content analysis methodology, beginning band method books were analyzed for the way authors include, identify, and provide additional context for cultural information related to the music and composers appearing in each method. An open coding process revealed the following four emergent themes: (a) authors position each book as supportive of a multicultural music education but do not include clear justifications for these values; (b) each book contains provenance labels to identify national origin or ethnicity of a melody or composer; (c) culturally diverse musical content is common but additional contexts are limited; and (d) when information is included it typically focuses on geography and language while lacking critical contexts. Findings are discussed through a contemporary lens of teaching music in the United States in 2021. These discussions problematize the broad and potentially harmful provenance label of “American”, question publishers’ decisions to print diverse cultural content without accompanying critical context, and acknowledge how language can create a nonacademic but socially significant hidden curriculum (Vallance, 1974). Recommendations for teachers of beginning band, music teacher educators, and implications for method book authors are shared. Limitations of this study include the role of the teacher in deciding what information to formally transmit to students and the complex politics of book publishing. Ideas for further research are stated.
Author: Daniel Taylor Publisher: ISBN: Category : Band music Languages : en Pages : 99
Book Description
Using a content analysis methodology, beginning band method books were analyzed for the way authors include, identify, and provide additional context for cultural information related to the music and composers appearing in each method. An open coding process revealed the following four emergent themes: (a) authors position each book as supportive of a multicultural music education but do not include clear justifications for these values; (b) each book contains provenance labels to identify national origin or ethnicity of a melody or composer; (c) culturally diverse musical content is common but additional contexts are limited; and (d) when information is included it typically focuses on geography and language while lacking critical contexts. Findings are discussed through a contemporary lens of teaching music in the United States in 2021. These discussions problematize the broad and potentially harmful provenance label of “American”, question publishers’ decisions to print diverse cultural content without accompanying critical context, and acknowledge how language can create a nonacademic but socially significant hidden curriculum (Vallance, 1974). Recommendations for teachers of beginning band, music teacher educators, and implications for method book authors are shared. Limitations of this study include the role of the teacher in deciding what information to formally transmit to students and the complex politics of book publishing. Ideas for further research are stated.
Author: Nicholas Cook Publisher: OUP Oxford ISBN: 0191606413 Category : Music Languages : en Pages : 203
Book Description
This stimulating Very Short Introduction to music invites us to really think about music and the values and qualities we ascribe to it. The world teems with different kinds of music-traditional, folk, classical, jazz, rock, pop-and each type of music tends to come with its own way of thinking. Drawing on a wealth of accessible examples ranging from Beethoven to Chinese zither music, Nicholas Cook attempts to provide a framework for thinking about all music. By examining the personal, social, and cultural values that music embodies, the book reveals the shortcomings of traditional conceptions of music, and sketches a more inclusive approach emphasizing the role of performers and listeners. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.
Author: William L. William L. Hamilton Publisher: Springer Nature ISBN: 3031015886 Category : Computers Languages : en Pages : 141
Book Description
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author: Daniel Taylor
Author: Daniel Taylor
Author: Nicholas Cook
Author: Edwin E. Gordon
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Author: Martin Clayton
Author: Paulo Freire
Author: James Garratt
Author: William L. William L. Hamilton
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Author: Edwin Gordon