| Title |
Western classical music development: a statistical analysis of composers similarity, differentiation and evolution
|
|---|---|
| Published in |
Scientometrics, April 2017
|
| DOI | 10.1007/s11192-017-2387-x |
| Pubmed ID | |
| Authors |
Patrick Georges |
| Abstract |
This paper proposes a statistical analysis that captures similarities and differences between classical music composers with the eventual aim to understand why particular composers 'sound' different even if their 'lineages' (influences network) are similar or why they 'sound' alike if their 'lineages' are different. In order to do this we use statistical methods and measures of association or similarity (based on presence/absence of traits such as specific 'ecological' characteristics and personal musical influences) that have been developed in biosystematics, scientometrics, and bibliographic coupling. This paper also represents a first step towards a more ambitious goal of developing an evolutionary model of Western classical music. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 25% |
| France | 1 | 13% |
| Georgia | 1 | 13% |
| United States | 1 | 13% |
| Unknown | 3 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 88% |
| Practitioners (doctors, other healthcare professionals) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Unknown | 29 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 20% |
| Librarian | 4 | 13% |
| Researcher | 4 | 13% |
| Student > Bachelor | 3 | 10% |
| Professor > Associate Professor | 2 | 7% |
| Other | 5 | 17% |
| Unknown | 6 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Arts and Humanities | 9 | 30% |
| Computer Science | 4 | 13% |
| Social Sciences | 3 | 10% |
| Agricultural and Biological Sciences | 2 | 7% |
| Business, Management and Accounting | 1 | 3% |
| Other | 5 | 17% |
| Unknown | 6 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 December 2020.
All research outputs
#4,617,528
of 22,971,207 outputs
Outputs from Scientometrics
#888
of 2,690 outputs
Outputs of similar age
#81,456
of 309,575 outputs
Outputs of similar age from Scientometrics
#22
of 56 outputs
Altmetric has tracked 22,971,207 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,690 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 309,575 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.