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abstract, anonymous data, charts, content, curation, data visualization, homework, major project, personal, personality test, reading, technology, video, wmd
Homework #9: Content curation, reading & blogging
“The point is that new tools in general, and Twitter in particular, greatly challenge the binary dichotomy of attention as something that is either given or taken away, distracted. Instead, these tools allow us to direct attention to destinations where it can be sustained with more concentration and immersion.”
I found this particular quote from the article interesting because it discusses how new forms of media do not fit in strictly cut categories, just like how “content” and “curation” are morphing and changing to follow those new forms of media. Platforms like Twitter and Tumblr are often used not only to show and discuss different topics of interest, but also to move attention to different ideas that may not be readily available immediately on the aforementioned media platforms. The second part of my selected quote summarizes that in a much neater fashion. For those of us who use social media as a way to get information and news, we don’t read the headline of an article written in a tweet and go, “Well, I guess that’s it!”. If it is genuinely alarming or intriguing, one would click on the linked article or web page to read further in depth of what was summarized or introduced in the tweet or post. Of course, curating certain content together could produce a certain response, such as an environmentalist consistently posting articles about climate change and endangered species and peoples. Someone who is exploring that individual’s page would see the patterns of the content they have collected together and see that they are concerned with the state of our planet. Maybe after noticing that pattern, they might look into the issues themselves and create their own curated lists of content that other people might become interested in as well. This fits well into the space of “directing attention” as opposed to “[giving] or [taking] away”, and it’s interesting to see how more and more posts and tweets fall under that category.
Homework #6: Data Viz Blogging
Who Old Are You?
This data visualization from informationisbeautiful.net takes in your date of birth and compares you to many, many other people and events in life using a dotplot. Each of the colors represents a type of event, usually corresponding to a certain person, while the black dot represents where you are on the visualization. For example, my age is 18, and right behind my dot is Frida Kahlo’s beginning of painting. The dots are placed along a vertical and horizontal axis, where the vertical represents the amount of people at that approximate age while the horizontal represents that age at which a certain event or associate person is placed. What I can see here is that I am right at the cusp of where many famous individuals had their start. What I find most interesting about this data visualization is that I can really see that most people had their start at an older age at me, which is somewhat reassuring as my peers and friends, along with myself, feel that they need to “catch up” with others at various competitive values, such as academics, career, sport, etc. I also like that despite the large amount of dots, it’s still very readable and shows a clear pattern.
Hot Days
This data visualization from flowingdata.com takes your hometown and birth year to output a series of line graphs that show the number of days on average per year where the temperature exceeded 90 degrees Fahrenheit. There are two axis: the vertical represents the numbers of days on average per year that were hot, and the horizontal represents each year that has passed (and to be passed). For example, I used my birth year (2000) and my birth town (Duluth, GA) and found that it was relatively hot that year, which made sense seeing as it’s Georgia. Then, the data visualization showed me that today, it would be an average of 42 hot days a year, which is an 8 day increase from the previous graph. The final graph shows a prediction for the next 60 years, which shows a significant increase in hot days per year and includes an approximate range since the statistics are predictions that cannot be pinpoint accurate. This data visualizations purpose is to show the trend of increasingly warmer and warmer summers due to climate change. I find it interesting that they used data like birth year and hometown because it’s more personal, so it’ll have more of an impact to those who are viewing the graphs.
Homework #4: Blog Post on O’Neil / DIKW
First Example: The Teacher Selection WMD from the chapter Intro
In this example, I will be deconstructing the teacher selection WMD from the introduction chapter. This algorithm, called IMPACT, was designed to separate “good” and “bad” teachers based on a scoring system of math and language skill teaching effectiveness. At the first level, data, the algorithm is aware of a low IMPACT score. At the second level, information, the algorithm knows that a low IMPACT score designates a teacher as “bad”. At the third level, knowledge, the algorithm has determined that a teacher it is calculating has received a low IMPACT score. At the fourth and final level, wisdom, the people interpreting the scores believes the “right” thing to do is to label that teacher as “bad” and fire them. The issue with this algorithm is that it does not factor in on outside factors that can affect how a student tests, such as home and family issues, disabilities, bullying, etc., which cannot necessarily be calculated by a computer program. It cannot determine what amount, if test results decrease, is due to a teacher or due to something else. As the text says, it is near impossible to be able to program each individual factor that can affect the scores. In addition, this type of algorithm is something that needs to be adjusted as issues arise, but because of the way school systems simply fire the teachers, it is unlikely that changes can be made.
Second Example: The Employee Selection WMD from the chapter Ineligible
In this example, I will be deconstructing the employee selection WMD from the chapter ‘Ineligible’. This algorithm was designed to test prospective candidates similarly to the “Five Factor Model” test, which tested for “extraversion, agreeableness, conscientiousness, neuroticism, and openness to new ideas”. Using Kyle, a young man with bipolar disorder, as an example, we can see how the algorithm can go wrong. At the first level, data, the algorithm is testing for certain characteristics in those looking to be employed. At the second level, information, the algorithm knows that certain characteristics are deemed undesirable, such as mental illnesses or disorders. At the third level, knowledge, the algorithm has determined that Kyle has bipolar disorder, and undesirable trait as it could possibly disrupt a working environment. At the fourth level, wisdom, the algorithm decides that the “right” thing to do is to deem Kyle unfit to work at the corporation he applied to. Where the algorithm went wrong is sorting based on the “desirable characteristics”. The issue with this algorithm is that it bases work eligibility based on one’s mental state. Not only is that illegal, but it doesn’t use other factors that an in-person interview would discover, such as education level, previous work experience, etc.
Homework #1: Reading: Stephen Kline, “What is Technology?”
The sentence I chose from the text was: “A sociotechnical system of use is a system using combinations of hardware, people (and usually other elements) to accomplish tasks that humans cannot perform unaided by such systems – to extend human capacities”. Personally, this usage of technology most closely aligns with my definition of technology. I enjoy that the definition is vague enough (by using the term “systems”) to encompass parts of the other usages of “technology”. Technology does not necessarily have to be hardware for it to be considered “technology”. The process of constructing computers, buildings, or anything that requires non-human aid can be called “technology”. The wheel, a classic example, is considered technology because it was a piece of man-made hardware, but also it helped humans perform tasks that they had difficulty with or could not do alone such as long-distance transportation or easier mobility of goods. It’s interesting to see what is considered technology under this definition. Other than the wheel, another piece of technology that seems atypical from the norm could be a bottle or water pouch. It’s not like a modern electronic device, yet it has helped humans travel longer distances, work for longer, and live for longer because they were able to have water or other liquids with them for easier access. Even clothing could be considered technology, as it helps humans retain body heat (or reduce it) and can enhance one’s aesthetic, but I would imagine that at that point it becomes almost satirical in its usage.