The data web of song selections can be analyzed to determine participant preferences, but not rationales behind the preferences. For example, “Track 18: Fifth Symphony” by Beethoven is the most selected song with 16 of the 21 participants choosing it (myself included). On its own that data might suggest a Western bias, fame and recognition of the song, or overwhelming preference towards it. Given the varied possibilities for the popularity of the selection, rationale is difficult, if not impossible to discern. All that is evident is that the track is widely chosen. The second most selected was “Track 3: Percussion (Senegal),” and a three-way tie for third with “Track 25: Jaat Kahan Ho,” “Track: Johnny B. Goode.” And “Track 14: Melancholy Blues.” These, combined with “Track 18: Fifth Symphony,” may indicate a Western bias towards music selection given three of the five tracks are Western, however, the possible rationale for selecting them remain the same, and no definitive reasoning can be deduced. Just as the rationale for selecting any given track cannot be learned through this data alone, nor is the reasoning for not selecting a given track apparent. The reasoning is too broad to be inferred through simple numeric interpretation. Quantitative data like this cannot give qualitative reasoning for its results, especially when nothing is known about the participants making the connections.

However, the weight of the top three selections, if combined with a search algorithm would favour Western results. If our class data were to be the only information used on a search for something like: “best music,” “top tracks,” “songs to launch into space” or “earth’s greatest hits,” the results would suggest, and reinforce Western dominance. If these were the results of such searches, not only would Western music be overrepresented in a search, but would likely further be entrenched by those searching. By selecting those tracks, they would add further weight to the results, burying the remaining pieces of music into web searching obscurity.

Similarly, if communities are recommended based on the degree of connectivity, the potential for new music to permeate into that community become increasingly small as fewer and fewer members have outlying selections. Given that there is no way to know the reasoning for the number of connections shared with other members in my community, the relevance of the community is also uncertain. Similarly, with no directionality to the nodes, the relationship between members is also unknown. While I may share several track selections with other members, having no sense of preference or value associated with those connections makes them difficult to analyze. My belonging to a community based on connection alone might be problematic.