This week’s task was quite interesting, albeit challenging, as I was unfamiliar with network theory beyond a foundational understanding of the web and the connectivity of web nodes through algorithms. I have also never used Palladio as a tool through which to interpret data and realized that I am much more familiar with charts, graphs and tables and more attuned to Google Sheets than a visualization platform like Palladio. Palladio provides us with the visualization of the Golden Record track selection data within which I represent a node when looking at the data as a whole, or seemingly an edge when looking at each group individually. While using this platform to interpret data was an interesting experience, I struggled to understand how to truly leverage the site options to help me make strong conclusions, even after independent research. Perhaps this course could aim to scaffold this particular activity further moving forward to set students up for greater success and discussion.
Looking through the visualized data I first sought to see which groups I was omitted from. I did not appear to be connected to Community 4 as I do not represent an edge connecting any of the song nodes. This makes sense as I did not select any of the tracks that are nodes represented in that community. A conclusion that could be drawn from this is that the individuals most connected within Community 4 may have had much different selection criteria for the tracks than I did. By contrast, individuals in that community may have had certain criteria that were similar to mine but may have assessed certain tracks using that criterion differently. This could be an interesting extension to visualize the criteria each participant used to guide their selections and cross-reference that with the tracks they selected.
By contrast, within Community 1 I seem to appear near the middle of the visualization and connect to four of the nodes. This points to the idea that other individuals in that community near the centre might have also had similar selection criteria for the activity. Namely, Bingying, Hassan and Louisa might share a similar interpretation of criteria. In Community 3 Hassan and I are again in a similar situation concerning the two tracks we both selected. In looking at all six communities at the same time, this connection between Hassan and I is again clear.
This data visualization makes connections explicit and suggests possible conclusions, but cannot identify, with certainty, why specific songs were more popular and others were not. The visualization outlines, for example, that Bridget, Hassan Nisrine and myself selected many of the same tracks. What is missing from this interpretation is why that is. It could very well be that we shared a similar set of criteria in selecting our songs, or perhaps our interpretations of our unique criteria led to similarities. As mentioned above, I believe that the only way to truly understand why certain song selections were more popular than others would be to include categories that influenced selection. In that case, individuals would not only be linked to the categories of selection but also to their selected songs.