Upon first glance of the data presented in the graph, it was an overwhelming amount of information spread across the web. With further dissection of the data into communities (and the help of Ernesto), I found myself situated in a group of six with Jessica, Katie, Jacey, Sage and Kayli. It was also helpful to filter by the size of the nodes to determine which targets held the most weight. In our group, we all selected Night Chant and Jaat Kahan Ho as one of the top 10 songs from the Golden Record. There were four more tracks that 5  of us shared in common and each of us had individual outliers that we chose. Upon further analysis of this data by reading my peers’ blogpost on their selection process, it would seem that these commonalities occurred because of our desire to cast a wide range of musical selections with more diversity. As Sage aptly explained in her justification process, “I wanted to create a ratio that was more representative of humanity”. 

This reasoning seemed logical for our grouping but upon deeper analysis of the entire dataset it appeared that these two songs had a high degree of connectivity for the entire class. Therefore, unlike my original assumption, it is not because we share these two songs that we are in the same group. That begs the question, why did the algorithm put us together in Community 4? Looking at the wider scope, it could be because of the number of connections between one or more group members that are similar. Our common null choices could also be considered in the algorithm that created these facets. 

The quantifiable data visualizations do not consider the qualitative nature of the selection process. Whereas each of our group members had different criteria to either include or exclude tracks, whether it be on emotional value, personal experience or ‘decipherability’ as Sage decided, there are multiple paths that can be taken to garner the same results. The political implications of this filtering criteria causes a discrepancy between the intent and the outcome. That is, opposing political groups may reach the same conclusion in different contexts as a result of missing or assumed data that is not aligned with the intent. Take for example the controversial topic of vaccination status.  One might make assumptions about a person’s educational background, political views and even religion without truly understanding the reasoning.  Even a topic as simple as mask wearing can cause tension between groups because of assumptions or misinterpretations for a person’s decision. These choices or results in data are not a direct reflection of one’s identity as it fails to consider the underlying intentions.