I was very pleased and surprised to learn that I was part of the largest community when looking at the Palladio data. As someone who studied music for many years, I really enjoyed the previous task, and had spent considerable time deliberating which songs to keep in and out of the limited ten.
Through looking at the data, I also learned that I had “correctly” chosen 6 out of the most popular 10 songs. Despite this having no real value, it was interesting to reflect on the sense of accomplishment this made me feel, despite having no real meaning.
The largest community contained 5 people and 22 total songs. Of those songs, 8 of them were not shared amongst any of the 5 members. For reference, the remaining communities consisted of:
- 4 people, 17 songs, and 6 solos
- 3 people, 18 songs, and 9 solos
- 3 people, 17 songs, and 8 solos
- 3 people, 19 songs, and 12 solos
- 2 people, 14 songs, and 11 solos
Despite the breadth of these statistics, the actual intention behind each persons’ decision remains unknown. Null choices can not be interpreted using this data, and even using the communities grouping can be misleading. For example, in my community, there were 8 songs that had “no connection” (they were displayed as solo nodes). Even though there were no connections in this community, some of these songs were in the top 10 most popular choices for the entire class, but by looking at just our community, you wouldn’t believe this to be the case.
If you read my notes on the curation assignment, you’d know that I had many considerations including: “country of origin, length, genre, and if the song was instrumental or vocal” (para 1, 2025). Sadly, this analysis is lost through this data, as it is for every member who participated.
Still, there are some data points that instill curiosity. For example, Jamie, Joan, and David all selected less than 10 songs. I wonder what their reasoning was for their decision making. Did they have a hard time deciding? Did they decide that they didn’t need 10 options to create a well-rounded set? We may never know.
Much like real life, these groupings show individuals with like interests, but also obscure much of the total picture. In one group, it may seem like a particular song was horribly unpopular, but if you explore other groups, that song may be very well-represented. Although looking at similarities can join us together, it can also create divide and alienation. It is important that we continue to challenge our assumptions about the communities we are in, and continue to seek outside information.
References
Wong, T. (2025). Task 8: Golden record curation assignment. ETEC 540 Tristan Wong. https://blogs.ubc.ca/twong540/task-8-golden-record-curation-assignment/