Task 9: Network Assignment Using Golden Record Curation Quiz Data

I found this week’s topics to be quite interesting. My partner, who studies computer science, overheard me listening to the videos this week and it opened into an awfully existential conversation of search algorithms and their potential. However, we also discussed the direction of these browsers and their auction of our data. As mentioned by Code.org. (2017), these search engines are using data we have not yet provided, like our location. This “attention economy” is cyclical and feeds itself, making me think about the boundaries of privacy (ETEC 540, 2025). The more information they collect about us, the more they are able to influence our perception of reality. Similarly, I remember learning about sociolinguistics and how it explores this topic even further. Although search engines are known for using geographic data from things like your IP, linguists can determine personal information like your origin, gender, age, and profession just based on aspects like your word choice, structure, and style of writing. As AI gathers more information and interacts with more human intelligence, how much does it already know?

After playing around with the data and watching the videos, I started to play with the data. I loved the flexibility of presentation visuals to show different stories. When playing with the data, you begin to notice patterns of what is relevant/helpful information, and what is not. However, I think styles like the graph are very limited in a 2D format like a computer screen.

The more I explored this data, the more I wished I had more information to use. The bigger questions that I had did not have the evidence (or maybe I should say data) needed to give a clearer insight. I wanted to know how personal social aspects affected the results: Which genre of music was most preferred? What era was more popular? That being said, without the additional information I wanted, the graph was already complex.

One thing that I noticed was that my choices were not the most popular tracks. This made me think about other aspects of decisions. When voting, I didn’t want to just choose the songs that I liked the best, I wanted to basically make a more concise playlist. Reflecting on my own process of deciding, I kept 2 things in mind: which songs do I believe best represent the good of humanity and how can I ensure that it represents the diversity of humanity. I do remember mentioning my surprise at the diversity of ethnicities and languages and also the inequality of genders, keeping that in mind, my responses may have also been biased in some ways.

Another way my bias may affect the data, but not explicitly show, is age. With music tastes and genres shifting throughout time, each generation will more or less gravitate to certain styles of music. Being 26, I have noticed most of my peers are typically older than me. With this assumption, I hypothesized that my votes would most likely not be in the majority. When looking at the results, I used the table and chose Row: Track and Dimensions: Curator, generated.
From this, I counted each vote for each track and tallied them up. To symbolize my choices, I added a *. Of the top 3 most popular tracks (T14, T3, T13), I only voted for 1 (T13). Of the top 8 most popular tracks (T14, T3, T13, T6, T5, T8, T25, T22, T23, T24), I only voted for 3 (T13, T5, T23). This averages out to me being 35% aligned with my peers.

T1-* 8
T2- 13
T3-* 3
T4-* 8
T5-* 10
T6- 11
T7- 2
T8- 10
T9- 2
T10- 10
T11- 6
T12- 5
T13-* 13
T14- 6
T15-* 7
T16- 1
T17- 14
T18-* 7
T19-* 6
T20- 6
T21- 5
T22- 9
T23-* 9
T24- 9
T25- 10
T26-* 5

Although I am unable to have the data that I would personally find telling, the social factors that I want to use are still not going to tell me the full story of my peers’ decisions. Did they have similar reasoning to me? Did they choose randomly? Were there other biases? I think these are the kinds of questions that spark the need for research and the need to look at data in new ways to find other ways of interconnectedness.

References:
Code.org. (2017). The Internet: How search works. [Video]. YouTube.

ETEC 540. (2025). ETEC_V 540 64A 2025W1 Text Technologies: The Changing Spaces of Reading and Writing. Master of Educational Technology. University of British Columbia. Week 9, p. 9.1.