The image on the right is a partial screenshot of the curator communities image. This image shows where I "sit" within the community. It is interesting to note that the orange and green communities are the smallest. The other communities have at least 4 members.
For this task, we were required to reflect on the outcomes of the communities created from the Golden Record assignment. It is interesting to analyze this data after a week of time between looking at the songs and materials. To be honest, I had to go back to my last post to remember what song choices I made for my Golden Record choices. Some song titles were familiar to me, but I had to do a “double take” to make sure it was in fact, my song choice. This makes me question the entire set of this data as a whole. For example, were any of my peers in the same boat as me- forgetful to our choices? Further, would this mean our (my) data is not as valid as others? If this is the case for a few of the participants, it makes me wonder about the validity of the data set. This has made me think further to what would happen if I were to rewrite this entire task; I wonder if I would pick the same choices? What is assumed in this data set is that the participants would make the same choice over and over again. The data shown does not take into consideration the background information relevant to a person’s choice. I think a few main factors should be considered such as age and gender (maybe ethnicity, too), as a start. Further information provided could lead to deeper insight of the participants’ choices. For example: mood, time of day, musical bias, etc. I’m sure there are a lot of factors that could be considered. The data presented in CLAS does not show us each participants “criteria,” as many have noted in their MET blogs. Most of us did not choose randomly- most people seemed to have a sort of method to their madness.
The three images show us different information and I had to refer to all three images to gain a clear picture of the data. The first image shows communities as dictated by colour as explained above in the image. This image is the easiest to see patterns as there are names and colour coded dots. The purple group is the largest with 5 community members, followed by light green and blue with four members in each. At first glance, the second and third images are difficult to navigate. It takes the eye some time to adjust to what it is actually seeing. It is difficult to follow the lines connecting the communities of people. In image 2, when zooming in on the image, it is almost impossible to exactly determine the width of each line. Of course, to the naked eye, some lines are thicker than others. Between image 2 and 3, I needed to do some cross referencing to fully understand what the lines meant. For one, I am assuming that the thickest line connects each participant to the next. I am also assuming that the thicker the line means the greater the connection between each participant.I would be interested to know what other people think about the thickness of the lines.
Image 3 changes from the coloured communities. The participants names are featured in the same colour, but surrounded by the song titles. The lines in this image connect to how many participants chose each particular song. This was very interesting data to see- this is what I was waiting for! Out of a random sample of participants (MET students), which song was the most popular? Or the least? I found that Soloman Islands was the least as it was only chosen by two participants.
The coloured communities demonstrate similarities shared among participants. It is interesting that I have the most in common with just one peer, Ben, as decided by our shared colour- orange. Although, what is shown on image three does not make this as clear as I originally thought. Our communities are spread out and my name is no longer grouped near Ben’s name. Upon visiting Ben’s MET page, I learnt that we do have a bit in common, which could play a role in why we have some of the same choices for our Golden Record. On Ben’s page, he notes “my song list was chosen to represent both a large range of geographical areas and as well emotions.” It too, was my goal to have a well rounded geographical choices. Interestingly, the data shows me that Ben and I only share 4 common choices. This is surprising to me. Ben and I are considered to be in the same coloured community, but we only share a 40% similarity. Ben and I have a few things in common: we are both teachers who enjoy using technology, and we love to snack. Interestingly, we both have used Google Sites for our MET page. This tells me that we share a common love for all things Google, and we share similarities in how we like to organize things. Although Ben and I have some shared interests in both being teachers, I am surprised that I was not a part of a larger coloured community. It was not rocket science how I chose my picks for the Golden Record top 10. I assumed many peers would pick a variety of diverse sounds, especially since the country of origin was easy to find either in the title or from a quick search. This tells me that the data shows that although the participants sampled (all MET students in the same course), there is still a variety of diversity among peers. It was interesting to see my peers’ criteria for choosing. More about this will be found on my Links to Peer’s Work page.
There is a bit of data not featured in any of the three images. As the reader, I do not have immediate access to the data of the songs that participants did not choose. I say immediate access because this could be analyzed and found out; however, it would take quite a deal of time by following the lines. The images created focus on what the participants did choose, not what they did not choose.