While I have worked on Google Docs for class projects and assignments in the past, yesterday’s exercise of a completely crowd-sourced, virtual essay was a first for me. Initially I was skeptical about how the process would work, and thought that it would either result in utter chaos or with individuals answering unrelated questions without any true collaboration. Despite being slightly overwhelming at times, I thought that overall the exercise was a positive one, and that contrary to what I thought would happen, there was a fair amount of virtual collaboration between individuals in crafting responses that naturally fit together. Additionally, at least in the A-K document, there was little interaction using Google Docs’ chat feature, and most of the edits seemed to occur somewhat organically. The overall exercise made me think of a virtual discussion based class, where a professor’s questions would be answered online and in real-time, with students able to comment on others’ posts and give other types of feedback remotely. Maybe this is the next step in eliminating the fear of speaking up in public that continues to be an issue for students. Another aspect of the crowd-sourced essay that I found interesting was the discussion that the A-K document group started below our responses. Those comments noted some similarities between the exercise and The Circle, namely the fact that everyone could observe and potentially monitor the other members’ level of participation. The discussion drew similarities between Google Docs and The Circle’s PartiRank program, but it also expanded further and highlighted the potential for other uses of individual’s data, much like how Eggers presents some of the innovations in the novel. Specifically, someone brought up the point that Google could analyze one’s writing style and sell it to colleges and hiring agencies, presumably to build a more comprehensive profile for an applicant. Although these comments were extraneous to the overall objective of the assignment, it was interesting to get some realtime feedback from other participants. While the group was very aware of the connections between the exercise and Eggers’ novel, such an experiment would be interesting to conduct with people who are unfamiliar with The Circle and some of its major social implications.
For my observations post this week, I decided to track the frequency of yawns throughout Tuesday’s and Thursday’s class. Everyone has heard at one point or another that yawning is contagious, and my aim was to see if this held true for our class. While I am certain that I missed a few yawns here and there, I counted 31 yawns during Tuesday’s class and 16 yawns on Thursday. Going into the week, I thought that I would record more yawns during Thursday’s class because people may be generally more tired in the latter half of the week. This hypothesis proved to be wrong, suggesting that maybe students are more fatigued in the first part of the week, as they transition from a relaxed weekend setting back to the daily grind of classes, homework, and extra-curricular activities.
Another reason for choosing to examine in-class yawning was to attempt to see how one person’s yawning influenced their peers. Throughout the two classes, however, I didn’t observe many instances of one individual’s yawning directly causing another person to yawn. What I did notice, however, was that based on my data collection, I was the class’s most frequent yawner. On Tuesday, I was responsible for 8 of the class’s 32 yawns (25%) and on Thursday I accounted for 4 of the 16 total yawns (25%). Additionally, on both days, the table I sat at had the highest total number of yawns. I believe these two observations support the idea that yawning is contagious. First of all, I was the most frequent yawner because I was actively thinking about yawning for the entirety of both class periods. Additionally, because of my high yawning instances, the people nearest to me also yawned more than any other table in the class. While I don’t have enough evidence to conclude this with absolute certainty, I will say that scavenging the room for yawns is a more tiring process than I originally thought. Finally, to accurately gauge in-class yawning, one would need to enlist the help of an independent recorder who was far enough away from the class so as not to skew the data collection.
In her article “Big Mother Is Watching You,” Anne Helen Petersen discusses the recent rise in popularity of wearable data tracking devices. Petersen highlights devices that run the gamut in terms of functionality, ranging form the mundane fitness tracker to devices made for children to track their elderly parents’ household routines. Within the article, Petersen also hints at how her own fitness tracker use has affected, and potentially influenced her behavior. She notes that she has become obsessed with her sleeping data, and that she even purposefully excludes certain nights from her data collection, so as not to ruin her nightly averages.
Later in the article, Petersen discusses the potential effects that fitness trackers and other wearable devices may have on the insurance and medical fields. Specifically, she speculates that if wearable devices become more sophisticated, they may be able to help identify certain medical conditions, thereby reducing the number of trips to a physician’s office. Wearable health trackers, pending some technological innovations, have the potential to eliminate the knowledge divide between the general public and highly-educated doctors. While it may be years before these innovations are in place, they would certainly impact the insurance industry as an individual’s riskiness could be more accurately calculated using their accumulated data.
While purposefully altering one’s own sleep data and the potential health industry changes associated with fitness trackers may not seem related at first, there do exist some important interactions between the two. The first effect that comes to mind is the placebo effect that could exist simply from wearing a fitness tracker. It is likely that someone who is constantly reminded of their physical activity will workout more and generally be more health-conscious. While this encourages healthy behavior, it may occur even when the their fitness tracker is not working properly. For example, even when the R65 application was down, I found myself feeling guilty for everyday that I didn’t work out, despite the fact that any exercise I got wouldn’t be recording anyway. Additionally, health trackers pose a potentially adverse effect on the insurance industry. If customer’s data is being mined for insurance purposes, then someone who artificially alters their data has the ability make themselves appear as a less risky investment than they truly are. With the rise of fitness trackers and the overall quantified self movement gaining traction, the consequences of our daily data become increasingly more interesting and controversial. While “forgetting” to wear our bands when we go to sleep may seem harmless now, this could become a real problem if fitness trackers continue their rise in prevalence in our daily lives.
In the discussion following Tuesday’s map making exercise, which required us to create three maps leading from Studio D to Nummit, Davidson Pizza Company, and Chipotle, a number of people mentioned how they had difficulty managing the scale of their maps. I encountered this scaling issue when I ran out of space while attempting to draw the path from Studio D to Nummit. Despite the distance between the two points amounting to only a few hundred yards and the fact that I make this walk on a daily basis, the other two maps that I drew were far more accurate. While I am clearly a terrible cartographer, I believe that my familiarity with the path from Studio D to Nummit actually hindered my ability to successfully depict the route. Because I know the route so well, I found myself trying to add every little detail to the map that I fit, and as a result, I had to drastically alter the scale on the map to fit Nummit on the page. Conversely, when drawing the other two maps, I had far better spacing and was able to fairly accurately show how to get from one place to the other. I believe this was the case as I was not as concerned with drawing every minute detail, but instead I focused more showing the route from point A to point B.
While others may not have had similar experiences drawing their maps, mine left me asking if too much information can be a detriment when constructing a map. Ultimately, this question relates to Monmonier’s article and highlights the tradeoff between accuracy and usefulness that all map makers face. I found through this exercise that the more information one tries to incorporate into a map, more difficult it becomes to use the map. On the other hand, too little information appearing on a map is potentially dangerous as the omission of an important detail could render a map completely ineffective.
While I collected data on some of the standard measures that have already been reported, I would like to use this Observations post to bring to attention the amount of laughter that occurred in class this week. On both Tuesday and Thursday, I counted 7 instances of laughter. The criteria that I used to determine a “laugh” was simply that more than one person in class had to snicker, chuckle, giggle or smirk at a particular event. While this may not be the best way to gauge exactly how funny various things were, I felt that but making it necessary for more than one person to be laughing at once, it would eliminate the potential that one person simply found something funny based on an inside joke or personal experience that related to whatever was happening in class.
Tuesday, February 10:
- Instance 1: A majority of the class laughed when Dr. Sample proposed that everyone raise their hand when he asked a question
- Instances 2, 3, and 4: The entire classes laughed during the first round of questions when those called on by Dr. Sample passed the question on to one another
- Instance 5: Many laughed when Mr. Sample mentioned that were would be trying something out another exercise (referring to the Fish Bowl exercise)
- Instance 6: Several people laughed during the Fish Bowl experiment when the first person “tapped in” to the conversation in the middle of the room and relieved someone of their duties
- Instance 7: A general chuckle occurred when someone pointed out that the undecided group, positioned in the middle of the room, did not initially have a seat to represent their opinion at the middle table
- “No taxation without representation” murmured someone in the class
Thursday, February 12:
- Instance 1: A few students laughed at the beginning of the Skype call with the r65 Lab workers as the group waved and said hello
- Instance 2: A couple of students laughed when the audio on the Skype call cut out for a few seconds and Mark Williams mentioned that he, “Didn’t quite get all of that,” referring to a statement made by Dr. Sample
- Instance 3: A few in the class laughed as Alex of r65 labs received a text and cleared it immediately while his phone was on screen-sharing mode
- Instance 4: Many in the class chuckled when Alex turned off screen-sharing from his laptop, realizing that we were looking at ourselves through his Skype feed
- Instances 5 and 6: Many groups at tables laughed when the Misfit bands were passed around
- Specifically, one group snickered as Dr. Sample suggested the reason we are allowed to keep the bands is because r65 Labs doesn’t want them back after they’ve been on our dirty wrists for 10 weeks
- The group I was sitting with laughed when we were deciding whether to wear the Misfit in addition to, or in place of a watch
- One noted, “This thing does everything besides telling the time”
- Instance 7: People laughed at my table when we read aloud the questions others had written about their data sets
1. Why are the telcon and memcon textplots in different shapes?
2. How do you quantify the distance and placement of the different words being used in the textplots?
3. Does this improve the archive if the overall visualization is not easy for someone to comprehend it?
4. What do the lines between words mean?
In her article “The Image of Absence,” Lauren F. Klein advocates for the use of data visualizations in recreating historical narratives. Specifically, much of her paper is spent analyzing Thomas Jefferson’s letter collection as catalogued in “The Papers of Thomas Jefferson” digital database. Her primary subject is a man named James Hemings, a slave of Jefferson’s who eventually became a chef at his French estate. Throughout her article, Klein notes that while Jefferson’s letter collection is extensive, researchers are rather limited in the potential claims they can make regarding the Antebellum period due to Jefferson’s narrow point of view. Furthermore, Klein suggests that data visualization methods and specific searches within the collection can elicit more telling information of the time period.
Klein notes that one of the main issues with any written work of this time revolves around the fact that they were published by white, landowning males. Therefore, even works written by slaves don’t yield an accurate portrayal of the networks and social structures that truly existed during Jefferson’s lifetime. Klein urges that in order to reconstruct these narratives, “We must look to the pathways of connection between persons and among groups, the networks of communication in which these men and women engaged, and the distributed impact of the labor they performed” (665). Here, Klein asserts, is where data visualizations can fill the gaps that individual letters and correspondences create. Perhaps the most interesting example of the role of data visualizations in her article, Figure 3 depicts the “networks of relations” embedded in a specific set of letters pertaining to James Hemings’ life. The figure, pictured below, was created by a computing process that identified groupings of words that resemble people’s names in 58 letters referencing Hemings. Klein concludes that figure exhibits the intricate relationships that existed between all members of Jefferson’s network, including his family, political correspondents, friends, plantation staff and his slaves. Examining Hemings’ node on the figure, it becomes clear that unlike the traditional narrative of the time, he was in correspondence with a variety of people, including Jefferson, Jefferson’s friends and even free plantation staff. Additionally, Klein notes that the significant amount of correspondence between Jefferson and the Hemings family suggests that he truly relied on his slaves and actively sought to communicate with them. The overall importance of the figure is to highlight the complex networks and interactions that are often difficult to see in such a one-sided historical narrative as the Antebellum period.