Author: snmarquez

Natalia Cecire’s article “Introduction: Theory and the Virtues of Digital Humanities” discusses the debates around the role of theory in the digital humanities which are centered around the relationships of saying and doing. What most interested me about Cecire’s article was the section on how “Claims about doing are economic claims.” Cecire demonstrates how the “epistemology of doing has come to be framed in strangely specific terms, with social consequences for how it plays out in the wider discipline.” She uses the examples of wording such as “hands-on,” “getting your hands dirty,” and “building” which offer a distinctly masculine view of what exactly constitutes doing. These words are distinctly different from other metaphors for doing, such as “weaving, cooking… or nurturing. ” Cecire makes the points that within the humanities are dimensions of praxis: performance and activism, which are commonly found in disciplines such as women’s and ethnic studies. The choice of digital humanities to use terms such as “digging” and “building” to describe doing as leads to “distinctive methodologies of digital humanities [to be] represented in comfortably industrial terms.” Cecire argues that though the digital humanities is increasingly represented as a “return to a (white, male) industrial order of union jobs and visible products,” it is also in the best position to “critique and effect change in a social form—not merely to replicate it.”

An example I found of the digital humanities being “implicated in the postindustrial ‘feminization of labor’” that Cecire mentions can be found in Shawn Wen’s article “The Ladies Vanish.” Wen discusses the “different class of workers” found at Google that worked for ScanOps, “the team that did the painstaking work of scanning texts that make up Google Books.” These workers were mostly black and Latino, and started their shifts at 4 am in order to keep them separate from the majority of predominately white workers on the Google campus. Google erases the laborers from its Google Books project, in favor of promoting the technology and “expansive virtual library.” Google is not alone in hiding the human labor. Amazon has a Mechanical Turk program which “hires people to do invisible work online—work which makes their client companies’ software look flawless.” According to Wen:

The New York Times reports that workers on Amazon’s Mechanical Turk earn an estimated range of $1.20 to $5 per hour on average. Even more controversially, the terms of service allow employers to “accept” or “reject” the work after they receive it, no questions asked. The company is allowed to keep the work after they “reject” it, but the worker is denied pay and receives a lower online rating, making it harder to obtain future work on the site… [To add to this,] a study by NYU professor Panos Ipeirotis found that almost 70% of Mechanical Turkers were women. How shocking: the low prestige, invisible, poorly paid jobs on the Internet are filled by women. Women provide the behind the scenes labor that is mystified as the work of computers, unglamorous work transformed into apparent algorithmic perfection.

These examples show the importance of why the digital humanities should critique and effect change, and why theory is a fundamental place to begin.

Matthew G. Kirschenbaum’s article “So the Colors Cover the Wires”: Interface, Aesthetics, and Usability” reminded me a lot of chapter one “What is Design” in Design for the Real World by Victor Papanek.

Kirschenbaum’s discussion of interfaces as “layers” and the “distinction between different layers of interface…and ‘content’ is one that runs counter to decades of work in literary and cultural criticism, where form and content are almost instinctively understood as inextricable from one another.” To developers, the interface is computationally distinct from the content it intends to portray. However, Papanek states, “Design is the conscious and intuitive effort to impose meaningful order.” By designing an interface, the developer is imposing an order on the content that cannot be separated from the visualization. The biggest question in design is whether the design should be functional or aesthetically pleasing. Papanek describes six parts of the functions of design in order to answer the question. These fuctions are as follows: method (tools, materials, processes); association (education, culture); aesthetics (gestalt, perception); need (goal formation); Telesis (technological bias); and use (as tool, as communication). All designers need to take these six functions into consideration in order to produce a functional and aesthetically pleasing design. However, many designers strive for a more concise statement that the six functions: they seek precision, simplicity. For Papanek, “the particular satisfaction derived from the simplicity of a thing can be called elegance. When we speak of an elegant solution, we refer to something that reduces the complex to the simple,” much in the same way developers seek to create an elegant yet simple interface.

An example of an elegant interface I have recently discovered is Freunde von Freunden’s website. In April, FvF published the article “Rethinking Storytelling and Usability: FvF’s Approach to Digital Publishing.” It details the development of a new interface “as a matter of usability in synergy with content and aesthetics.” Since FvF publishes many interviews with guests that involve exploring their neighborhoods and studios, they decided to redesign the individual story pages that “interweave visuals with writing.” In the article FvF discusses their steps in developing the new interface, including a new feature they had to develop from scratch in order to “combine image, text, and video into seamless stories.” This article directly ties back to Kirschenbaum’s about the difficulties of creating interfaces, as well with Papanek’s writing on successful design.

Sara McLafferty’s, “Women and GIS: Geosptial Technologies and Feminist Geographies,” discusses the intersection between feminism, GIS technologies, and the impact of these technologies in women’s lives. McLafferty mentions the shift from a “pro-techonology” stance to an “anti-technology” stance within gender and technology discourse, primarily caused by the view that technology merely “perpetuate[s] and reproduce[s] gendered social relations” rather than liberating women from said constructs. However, there is a more nuanced view that “acknowledges technologies can have both positive and negative impacts at the same time…[these] vary among diverse social groups.” The way in which technologies are either positive or negative depend exclusively on why these technologies develop, and “how, where, and by whom [this technologies] are used.” Negative uses of technology can be most prominently seen when technological tools are used in attempts to control or monitor others for the sake of exerting power or dominance.

McLafferty mentions the increasing presence of geographical technologies within the realm of surveillance and monitoring. She mentions “closed-circuit TV cameras, high-resolution satellite imagery, tracking devices, and cell phones” as examples of specific instances of surveillance-monitoring equipment. However, since the publication of McLafferty’s article in 2006, cell phones have become capable of much more, and thereby contain more sensitive information. The sheer amount of data collected and retained within smartphones can lead people with malicious intentions desiring access over them. This is especially likely to happen with women who are in domestic abuse situations.

In NPR’s report, “Smartphones Are Used To Stalk, Control Domestic Abuse Victims,” the various ways abusive partners use technology for negative use is discussed in detail. According the report, “cyberstalking is now a standard part of domestic abuse in the U.S.” Many abusive partners use spyware and other tools to monitor domestic abuse victims who either attempted to leave and are in shelter situations, or are still within the abusive relationship. Many domestic abuse counselors require new arrivals to participate in a “digital detox,” which requires a complete shut down of a cellphone’s GPS and Wi-Fi, as well as staying away from Facebook. This is because:

Eighty-five percent of the shelters [NPR] surveyed say they’re working directly with victims whose abusers tracked them using GPS. Seventy-five percent say they’re working with victims whose abusers eavesdropped on their conversation remotely — using hidden mobile apps [such as MSpy]. And nearly half the shelters [NPR] surveyed have a policy against using Facebook on premises, because they are concerned a stalker can pinpoint location.

This NPR report is a modern day example of geoslavery, “in which the master coercively or surreptitiously monitors or exerts control over the location of another individual.” While tracking can be beneficial for parents wanting to make sure their children are safe and accounted, when used by the wrong people for the wrong goals, the results can be horrifying, as seen in NPR’s example. This report shows the ways technology can be used to control and surveil woman in an exclusively gendered way.

Sara McLafferty, “Women and GIS: Geosptial Technologies and Feminist Geographies”

Aarti Shahani, “Smartphones Are Used To Stalk, Control Domestic Abuse Victims.” NPR. NPR, 15 Sept. 2014. Web.

Scott Weingart’s, “Demystifying Networks” discusses the basics of networks and the power of network analysis—when used correctly. Creating a network visualization can be done for most projects, but using the correct methodology for these visualizations is incredibly important as well. Because networks are “any complex, interlocking system,” which reduces to “stuff and relationships,” network analysis can illuminate relationships in large sets of data. Online, social networks are formed around almost anything, from intellectConnect to Goth Passions to Facebook. The objects studied within these networks are considered “interdependent rather than independent” from one another and require “relationships [in order for researchers] to understand” what’s going on within the network. The “stuff” within the network defines what kind of network it is. For example, the “stuff” within Facebook is people and these individuals have different attributes (such as DOB, Location, High School, etc.) and create various multimodal networks. Every individual has a relationship with someone else within Facebook, creating a relationship, and each type of relationships have a type of edge, “defined…by the nodes they connect.”

However, can relationships be seen even with individuals are not within the Facebook network? A study, “One Plus One Makes Three (for Social Networks)” shows that connections can be deduced between members and non-members through member’s confirmed email contacts. The study found out that:

Social network platforms…have direct access to two different sets of relationships: on the one hand, the mutually confirmed contacts between platform members; and on the other hand, their members’ unilateral declarations of their acquaintance with non-members. The edges in both are an abstraction and a subset of the edges in the latent social graph… with the help of machine learning, social network operators can make predictions regarding the acquaintance or lack thereof between two non-members with a high rate of success…These are the first results on the potential of social network platforms to infer relationships between non-members.

This study exemplifies the power of social network analysis, as even those individuals who have chosen to not participate in a social network (in this case Facebook) can be inferred by machine technology as being connected to a member of said network. No matter what, individuals are unable to escape social networks, for being an individual requires the need to engage with at least one person a day, and these interactions inevitably lead to connections. Even when not participating on a social networking site, these connections can still be predicted. There is no safety from the net!

Horvát, Emöke-Ágnes, Michael Hanselmann, Fred A. Hamprecht, and Katharina A. Zweig. “One Plus One Makes Three (for Social Networks).”PLOS ONE. PLOS ONE, 6 Apr. 2012. Web.

Scott Weingart, “Demystifying Networks.” Web.

In “Humanities Approaches to Graphical Display” Johanna Drucker stresses the importance of remembering that the “humanities are committed to the concept of knowledge as interpretation, and, second, that the apprehension of the phenomena of the physical, social, cultural world is through constructed and constitutive acts, not mechanistic or naturalistic realist representations of pre-existing or self-evident information.” This is especially important to remember when using digital visualization tools in digital humanities endeavors. Many data visualizations seem to argue for an exclusive and narrow way of viewing the world based on assumptions of knowledge thought to be shared by the same groups of people viewing the visualization. In turn, these assumptions usually dilute the complexities of the external and ‘real’ world, in order to attempt to answer large questions with easily digestible graphics. This kind of visualization not only removes layers of complexity from the capta presented, but also assumes a role of neutral knowledge that is deceitful. When in this form, many types of differing knowledges can become hidden and the presented narrative can become static—in direct violation to the type of discourses humanities disciplines seek to encourage.

While not deliberate, an example can be seen in Katie Leach-Kemon’s article “Visualizing the surprisingly massive toll of suicide worldwide.” A data visualization titled “Top 20 causes of premature death in females, 2010” lists self-harm as number three in both 1990 and 2010, with the mean rank increasing in 2010. What is not immediately apparent is how exactly a female is defined as a female. In this example, it seems a female can only be between the ages of fifteen and forty-nine to be considered, which raises the question—are females below or above this age range considered female? What characterizes a female—the ability to procreate? Or is this data merely restricting the age set in order to get a smaller result? Why would this be the case if one wanted to know how many females committed an act of “self-harm”? How is female defined different than male in this data sample? As seen in Drucker’s paper, “the assumption that gender is a binary category, stable across all cultural and national communities, is an assertion, an argument. Gendered identity defined in binary terms is not a self-evident fact, no matter how often Olympic committees come up against the need for a single rigid genital criterion on which to determine difference.” What is also unclear is what exactly is defined as “self-harm”. Labeled under “injuries”, “self-harm” is included with “road injury,” “fire,” “interpersonal violence,” “drowning,” and “forces of nature.” If a female between the ages of fifteen and forty-nine set herself on fire and died, would this be included under self-harm or fire? As Drucker stated, “the more profound challenge we face is to accept the ambiguity of knowledge.” We must constantly repeat her “refrain–that all data is capta.”

Drucker, Johanna. “Humanities Approaches to Graphical Display,” Digital Humanities Quarterly 5, no. 1 (2011)

Leach-Kemon, Katie. “Visualizing the Surprisingly Massive Toll of Suicide Worldwide.” Humanosphere. 18 Aug. 2014. Web.

After reading Data + Design, I immediately remembered an example of a particularly effective combination of data & design I had seen several months ago, an interactive map of major South Asian migration flows. What particularly makes this infographic effective is it’s place within the context of the entire website. Striking-Women, “an educational site about migration, women and work, workers’ rights, and the story of South Asian women workers during the Grunwick and Gate Gourmet industrial disputes,” seeks to highlight a facet of history that is not well known or often discussed in mainstream circles (Striking-Women). The homepage of the site highlights four distinct issues: migration, women and work, rights and responsibilities, and strikes. Each section includes an introduction, relevant historical background, and present-day issues. The migration section is the only one with an infographic. This infographic allows the user to explore various migration flows by allowing users to click on a specific migration flow to learn more about it. For example, by clicking the solid blue arrow that leads from South Asia to Canada, a webpage replaces the map and details the history of “Post 1947 migration to US, Canada, Australia and New Zealand.” I found the map not only educational, but visually striking as well. I could see why “Maria Popova…said that data visualization is ‘at the intersection of art and algorithm’” (Data + Design). Laid out like a physical folded map and highlighting several specific countries, the different colored arrows illustrate movement otherwise invisible or ignored. In many ways, this map harkens back to the “native essence” of data visualization—especially answering questions of “‘Where am I?’ [and] ‘How do I get there?’” This map and it’s included informational pages helps to illuminate the reasons why one finds large numbers of South Asians in the UK and the Gulf States, among other countries.

Finishing Data + Design helped me understand the sheer amount of work that must have gone into the map of South Asian migration flows, as well as the well-thought-out nature of it’s design. The reasons the site creators chose specific colors, fonts, and arrows became clearer to me after completing the design section of the book, as I never would have thought serif would be more distracting to readers than san serif! I also was able to note the slightly 3D nature of the map after reading about the dangers of using 3D. However, in this cause 3D seems to help solidify the nature of the map as a map creating the appearance of folds. I am now excited make my own data visualization!

What stuck me most about the article “How Netflix Reverse Engineered Hollywood” were how many comments lamented the fact that despite the prevalence of ultra-specific altgenres, many users are only given the same suggested movies over and over. Because the function of these altgenres is to intimately personalize the film selections for a highly specific viewer, viewers are only given a select amount of options by the algorithm created by Netflix, limiting the immediate scope of their film watching. One user commented, “[This] explains why Netflix has steadily made its search function harder and harder to use. It really does not want to empower end-users, it wants to effectively program content for you… Some must be more profitable than others; hence those are the ones you are spammed with… The missing element is how profitable each and every stream might be.” While I am not sure about the factual accuracy of this comment, it does remind me of a similar site that attempts to create personalized content to enhance revenue: Facebook.

The similarities of Netflix and Facebook lie in the “design of the software system that supports them. How that software functions is the result of decisions made by programmers and leaders within the company behind the website” (Grosser). Netflix is designed to suggest films that you would want to watch based on your previous watch history. This leads to personalized streams and, most likely, increased at revenue for Netflix. Facebook is structured in a similar personalized way, but while many “personalities” can use one Netflix account, Facebook’s interface forces the user to realistically portray themselves online the same way they would as if they are in real life. It requires the use of a real name, location information, schools and jobs held, and what the music and movies one likes. According to Grosser:

This ideological position of singular identity permeates the technological design of Facebook, and is partially enforced by the culture of transparency the site promotes. The more one’s personal details are shared with the world, the harder it is to retrieve or change them without others noticing—and thus being drawn to the contradictions such changes might create. This is further enforced by the larger software ecosystem Facebook exists within, such as search engines, that index, store, and retain those personal details in perpetuity (Blanchette et al., 2002).

The personal details Facebook collects leads to a data-mining trove, and allows Facebook to use this information to target the user with personalized ads, much in the same way Netflix uses previously watched films to recommend movies a viewer will most likely watch. Both of these website’s software are what allow them to be so successful in marketing to specific interests, but also limit the variety of “interests” displayed, thereby regurgitating the same limited types of objects—-be it movies, or ads.