The DOI matches its Crossref record.
Music Making on YouTube
Abstract
People are making music at their leisure and publishing it online. YouTube has provided a space for musicians to publish multitrack music videos, join collective musical ensembles, and collaboratively perform with others. This chapter explores three trends of how musicians are creating music videos and forming virtual ensembles and music making communities: they are showing off their skills through music videos; they are creating videos to join large collective multitrack ensembles of hundreds or even thousands of others; and they are actively collaborating with small groups to create mediated performances. Collective and collaborative music making on the Internet are not only happening among grassroots amateur musicians, but also through educational and commercial institutions. Music making on the Internet allows for global interactions and collaboration, where people come together and enjoy music recreationally, unbound by time and space.
Overview
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In the citing paper
“In addition to listening activities, scholars have highlighted other preferred interactions with music such as popular music groups or lessons (Miell & Littleton, 2008), DJ/music production (Rambarran, 2017), perform/compose to share online (Cayari, 2017), perform in large bands, choirs, and orchestras (Elpus & Abril, 2019), and songwriting (Tobias, 2015).”
From Understanding Participation in Secondary Music Classes: A Literature Review (2020) · cites this paper as Cayari, 2017
Citations
Cited by
Understanding Participation in Secondary Music Classes: A Literature Review
Pendergast, S. (2020) · Update: Applications of Research in Music Education · doi:10.1177/8755123320928479 · Cited by 13
Metadata
- Authors
- Cayari, C.
- Venue
- Oxford Handbooks Online
- Publisher
- Oxford University Press
- Published
- 2017-01-10
- Cited by
- 5
- References
- 0
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
