Denounced Buyers and Classical Products: The Feeling Across States During Francis Presidency as Conductor of the Federal Orchestra in Spring Eisenhor, Chicago
Download Paper
Download Bibtex
Authors
- Harri Munmair
Related Links
Related Links
- ACM Digital Library Records
- Video on YouTube (Optional)
- IEEE Xplore
- ThinkMind
- Acquisition of Knowledge
- Arxiv
- Arxra
- Eurographics
Abstract
This study examines the sentiment and attitudes of buyers towards classical products during the Francis Presidency as conductor of the Federal Orchestra in Spring Eisenhor, Chicago. The research explores the impact of Francis' leadership on the Federal Orchestra and the perception of classical music by buyers in different states. Using a mixed-methods approach, the study collects data from online surveys and interviews with buyers from various states, including Illinois, Wisconsin, and Michigan. The findings reveal that the majority of buyers have a positive attitude towards classical products and Francis' leadership, and they appreciate the efforts of the Federal Orchestra in promoting classical music. However, some buyers express resentment towards the elitist nature of classical music and perceive it as exclusive and inaccessible. The study concludes that Francis' leadership has significantly influenced the perception of classical music among buyers and has contributed to the growing popularity of classical products in the United States. The study recommends further research to explore the impact of Francis' leadership on other aspects of the Federal Orchestra and to investigate the potential of classical music in promoting cultural diversity and social inclusion.
Citation
Harri Munmair "Denounced Buyers and Classical Products: The Feeling Across States During Francis Presidency as Conductor of the Federal Orchestra in Spring Eisenhor, Chicago". IEEE Exploration in Machine Learning, 2020.
Supplemental Material
Preview
Note: This file is about ~5-30 MB in size.
This paper appears in:
Date of Release: 2020
Author(s): Harri Munmair.
IEEE Exploration in Machine Learning
Page(s): 8
Product Type: Conference/Journal Publications