Author: sarah wong

This was so far one of the most difficult tasks I have had to complete for the class so far. I spent at least a week trying to think about how to analyze the data. It was rather difficult because there were so many ways that the data could be sorted.

Once sorted, I did not feel that I was familiar enough with my classmates to allow for a complete analysis. For example, some data that would have been useful would be age, background, etc … I tried to look at my classmates’ websites but not all of them have an about me page (which I would suggest making it one of the tasks for future courses) and trying to look at everyone’s work at the same time was overwhelming.

Every time I thought I found some sort of connection, there would be something else that did not make sense. I remember reading from our chat group that one student did a lottery for choosing the ten pieces which makes trying to analyze the data even more complicated. Despite the many challenges, I tried to analyze the data that was given to me in a number of different ways to the best of my ability. 

Using Palladio, the first thing I did was to find the person who was most similar to me. It was a coincidence that Helen was closest to me and that I used her post for one of my linking assignments because we were both talking about the story of how we met our husbands. When I looked at Helen’s explanation of song choices, I found that we were so different and yet had a similar way of classifying songs. Helen has no background in music while I do but we both choose to curate music from different places around the world. Due to my science background, I chose a systematic approach to choosing the songs and picking one from each continent while Helen’s humanities background lead her to pick a couple of songs from areas around the world but not necessary continents. 

Next, I was interested to look at who I have the least in common with. There were three people who shared only three songs with me and I chose to analyze one of them. I was surprised to see that from the data, Shaun and I only shared so few songs in common. Looking at his choices, I would have thought we had more in common because we both talked about choosing songs from different continents and also songs from different musical eras. I really liked how Shaun even went into depths of comparing different songs from the same continents and also paying tribute to different instrument voices. This is not something I thought of doing. However, one thing did bother me is that while he mentioned grouping songs in different musical eras, he has only included one era which is the classical period but has put the wrong composers in that period. This is something only people with a music background would notice.

Next, I went to find my group and look at those who made selections closest to my own. I wanted to try and find similarities and differences between who we were. I was in a group with Laura and Kristin and they both shared four song choices with me. I was not quite sure how we were put into a group because to me, having four out of ten choices is not that similar. I was curious to see if we have any similarities in our background but did not find any information about their background on their blog. I next looked at how they both chose their list of ten pieces for the golden record.

Laura originally wanted to do something similar to me which is “progressing from the more traditional tone to rock”. It was not as technical as my analysis but we had the same idea. She did change her criteria to “focus on global and temporal diversity, and the human voice.” It was quite different from how I chose my songs.

Kristin, on the other hand, had a very systematic way in choosing her songs, which I appreciated. She mainly went for songs that have a different tone. What bothered me was how she chose the classical songs. Again because she did not have a music background, at least she did not mention that she did, she grouped composers together that were from different eras and said that they were basically the same thing. I think it was only by chance that Laura, Kristin and I were put into the same group. I do not think we have that many similarities. 

Lastly, I took a look at the most and least popular song choices from our class. I was surprised that every single piece was chosen at least once. The most popular song chosen from our class was Beethoven’s Fifth Symphony. It was not a surprise to me as this piece had a significant cultural influence due to the time that it was written. It had a long lasting impact on various different cultures. Even for people without classical music training know about the motive of the song, “dit-dit-dit-dah”. It is still widely used in different scenarios and cultures. 

I was surprised to see one person, Daniella who chose New Guinea’s Men’s House Song. I tried to look at Daniella’s reasoning behind choosing this piece and looked into her background. There was not much about her but she did say she chose her songs based on the “emotional effect” on her. When I looked at the list of 27 pieces, I didn’t choose Men’s House Song because I did not like how it sounded.

To take this further, people might subconsciously not pick this piece due to the title which was gender specific. In 2017, the Me too (#MeToo) movement started. This is a movement that promotes feminism and targets men due to sexual harassment and abuse. Many women stepped into the light to publicize allegations of sex crimes commitment by powerful men. Although this might be far fetched but with a population of over 50% female in our class, it might have something to do with why this piece was chosen only once. 

After a long data analysis, I was able to learn a few things about my classmates using indirect data from their song curation. There was a diverse amount of criteria which my classmates used to classify which songs seemed the most important or most appropriate to them. There were some overarching criteria that seem to be due to their cultural significance such as the Fifth Symphony. Its influence can be quantifiably measured by the amount of selections it received. The least selected song can also derive its lack of selection through social influences and current events. I found it quite interesting to compare and contrast my own selections and the selections of my classmates as the dateset provided a good source for identifying cultural, social and personal differences.

Instead of explaining why I choose each song using 3 – 5 sentences each, I am going to explain my thought process behind how I narrowed down my 10 selected songs.

In addition to being a science high school teacher, I have a strong background in music. I used to teach piano and music theory as a side job. Part of music theory is music history, so I am familiar with how to categorize songs based on the composer, era, genre, etc …. 

I found it quite difficult to curate 10 pieces of music that represented the entire world because I couldn’t simply just split the world into 10 different parts. What I did at the beginning was forming a chart that broke down each song into the composer, title, location of where it was composed, the genre and the year it was composed. After spending some time researching and listening to each song, I had the idea of having two main categories of songs. The first five songs were chosen based on the era or year of composition and the second five songs were chosen from five of the continents of the world. 

When I studied music, the history courses were based on music eras. The main eras are medieval, renaissance, baroque, classical, romantic, and postmodern. I tried to choose at least one song that represented each era with the exception of the medieval and renaissance. I also tried to choose songs that were from different genres. These are the first five songs that I chose based on era:

Baroque: Bach, The Well-Tempered Clavier, Book 2, Prelude and Fugue in C, No.1. Glenn Gould, piano.

Classical: Beethoven, Fifth Symphony, First Movement, the Philharmonia Orchestra, Otto Klemperer, conductor.

Romantic: Stravinsky, Rite of Spring, Sacrificial Dance, Columbia Symphony Orchestra, Igor Stravinsky, conductor. 4:35

Postmodern Jazz: “Melancholy Blues,” performed by Louis Armstrong and his Hot Seven. 3:05

Postmodern Rock and Roll: “Johnny B. Goode,” written and performed by Chuck Berry.

For the second five songs, I chose one song that represented each continent with the exception of Antarctica and Europe. I did not include Europe as most of the songs from the first category are from Europe and I did not include Antarctica as I don’t think there were any pieces from that region. 

Africa: Senegal, percussion, recorded by Charles Duvelle.

Oceania: Australia, Aborigine songs, “Morning Star” and “Devil Bird,” recorded by Sandra LeBrun Holmes.

North America: Mexico, “El Cascabel,” performed by Lorenzo Barcelata and the Mariachi México.

South America: Peru, wedding song, recorded by John Cohen.

Asia: India, raga, “Jaat Kahan Ho,” sung by Surshri Kesar Bai Kerkar.