Incompatibility of Dataveillance and Relationships

Eggers highlights the complications arising from dataveillance in relationships, seen when Mae’s personal information is revealed to Francis through a search program called LuvLuv. This program utilizes an algorithm to search for Mae’s interests and dislikes, while “[analyzing] for relevance,” in order to create a virtual profile for suitors (Eggers 123). LuvLuv only turns up information that Mae “openly offered” or that was collected through her use of TruYou, highlighting her active role in producing the data LuvLuv aggregates. Understandably, Mae is uncomfortable with “having a matrix of preferences presented as [her] essence,” which she notes is not completely accurate (126).

This passage highlights the benefits of having a certain level of initial ignorance when in a relationship, as personal information must be learned through getting to personally know someone. Even though she provided this information publicly, it seems as though forgoing the process of becoming close with someone is unnatural. Moreover, simply collecting information about someone does not equate with truly getting to know that person, which requires time and trust. As Mae’s ex notes, Circle creates programs that “manufacture unnaturally extreme social needs,” highlighting how this technology is overstepping the bounds of normal dating behavior (134). Although one could argue that people use dating websites today, which essentially reflect LuvLuv’s goals, I would say that the active collection and creation of an online profile by the individual differs from an external source doing so for you.

Tracking the Fluidity of Conversations in Class

During Thursday’s class (3/12), I aimed to track the fluidity of conversations across different tables. Specifically, I wanted to see which tables participated most frequently and which tables most frequently interacted with each other. I listed the names of one person at each table to allow the viewer to orient the room. Additionally, I connected the lines at central nodes, when the conversation was shifting across tables. I define conversation as a continuous exchange between different tables. The single lines represent when only two tables talked, most often seen when Dr. Sample asked a question and a student responded. The longest conversation occurred when discussing the topic: What does connected mean to you?. There are some limitations to my observation: 1. I did not account for two people at the same table speaking consecutively 2. I did not track the time in which conversations took place. 3. There is a lot of “noise” because of the number of conversations shown and the size of the paper on which I tracked the conversations.


Ill Effects of College Scattergrams

When looking at a map, Mark Monmonier encourages users to be critical of the subjective influences that have shaped it, emphasizing the undue respect maps receive as compared to other data visualizations. Specifically, Monmonier states that “maps, like numbers, are often arcane images accorded undue respect and credibility,” reflecting his frustration with individuals’ lack of skepticism (Monmonier 3). Well, Monmonier may have been disappointed with my habit of obsessing over college scattergrams during my junior and senior years of high school.;jsessionid=EC6AE4ABF835738BEA2A1BC9B2F08D94.tomcat-main01?id=198385&collegeID=198385&collegeName=Davidson%20College&isForProfitCollege=false

For those who do not know, college scattergrams “are collected data points graphed to show the GPA and test scores of applicants to the college, indicating their accepted pool of students in a visual form” (McNamara). To be honest, I used to scour through these scattergrams for hours, switching from college to college on sites like Cappex , ultimately affecting my decision to apply to certain schools based on the probability that I would be accepted. I fell prey to those who designed these scattergrams, who stripped me of my identity beyond my GPA and test scores. Similarly, scattergrams do not capture the importance of other factors that determine college acceptance, such as extracurriculars, legacy status, college essay, teacher recommendations, etc.. Monmonier would also criticize how rejections are seen as red dots, while acceptances are seen as green, which “mislead the map viewer” into thinking they are an ‘other’ or not worthy enough to be a part of the green dots (Monmonier 3). Therefore, this “visual reduction” of a student negatively impacts their self worth by making it seem as though test scores and GPA are the most prized attributes colleges focus on (Manovich 38). In analyzing data visualizations, we should be mindful of how they only offer a limited and biased view of the bigger picture.

Additional Sources Used:;jsessionid=EC6AE4ABF835738BEA2A1BC9B2F08D94.tomcat-main01?id=198385&collegeID=198385&collegeName=Davidson%20College&isForProfitCollege=false

Hodgepodge of Observations from 1/27 and 1/29

Tuesday (1/27):

  • Words written on the board by Dr. Sample: Dataveillance, surveillance, and superpanopticon (this word was underlined)
  • 18 comments written on the board by students, responding to Foucault’s statement, “Visibility is a trap”
  • First interactive class exercise and first time Dr. Sample has recorded us
  • 3 people wearing hats to class
  • Class finished at 2:52PM

Thursday (1/29)

  • Words written on the board by Dr. Sample: Inscribe, writing (written underneath inscribe), represent, and depict (written underneath represent)
  • No comments written on board by students
  • First YouTube video shown in class
  • In preparation for the quantified self assignment, it looks like there are 18 IOS devices, 8 Androids, and 4 Others
  • Ended class on the idea that the act of observation changes the object being observed

General observation from both days:

With some exceptions, it seems as though the class organizes its seating arrangement by gender (all 3 girls at one table), fraternity, athletic organization, and college year.