• HC Visitor
Skip to content
Information Ecosystems
Information Ecosystems

Information, Power, and Consequences

Primary Navigation Menu
Menu
  • InfoEco Podcast
  • InfoEco Blog
  • InfoEco Cookbook
    • About
    • Curricular Pathways
    • Cookbook Modules

Big Data

Chris Gilliard Visits the Podcast: Digital Redlining, Tech Policy, and What it Really Means to Have Privacy Online

2021-04-06
By: Jane Rohrer
On: April 6, 2021
In: Chris Gilliard
Tagged: Big Data, data pipelines, digital privacy, Ed Tech, Education, Information Ecosystems, race, racism, surveillance

The history of surveillance in the United States is a long one. Our guest for the podcast on March 31, 2021, Dr. Chris Gillard, studies this very fact; Dr. Gillard’s scholarship focuses on digital privacy, institutional tech policy, surveillance capitalism, and digital redlining—a term that he defined on the podcast as “the creation and maintenance of tech practices, policies, pedagogies, and investment decisions that enforce class boundaries and discriminate against marginalized group.” As many of our Seminar guests have attested, too, access and relationships to contemporary digital technologies falls along racial, gendered, and classed lines, and the Internet—and the tools we use to access it—are made overwhelming by and for wealthy, straight white men in urban environments. And as Dr. Gilliard points out, access to the Internet is not the only thing historically minoritized groups are robbed of; these groups are also overwhelmingly stripped of their autonomy and privacy online. Although worries about CCTV and post-Patriot Act wiretapping seem especially twenty-first century, eminent scholars have recently illustrated how the very foundation of our nation, including its formation of racial and class differences, depended on the institution of surveillance. In her groundbreaking Dark Matters: On The Surveillance of Blackness, Simone Browne makes clear the connections between “the Panopticon, captivity, the slave ship, plantation slavery, racism, and the contemporary carceral practices of the U.S. prison system,” illustrating how contemporary surveillance technologies of all kinds have been formed and informed by the U.S.’s methods of policing and categorizing Black life under slavery (Browne pg. 43). This is evident all Read More

A 19th Century Doctor's Visit

Numbers Have History

2021-03-25
By: Jane Rohrer
On: March 25, 2021
In: Christopher Phillips
Tagged: artificial intelligence, Big Data, history, history of science, Information, medicine, precision medicine, sawyer seminar, STEM

Dr. Christopher Phillips on the Histories of Statistics & Data in Medicine On March 17, our podcast hosted Dr. Christopher Phillips, a Professor and Historian of science, medicine, and statistics Carnegie Mellon University—and also a member of our Seminar! Beginning in the Fall of 2019, Dr. Phillips joined in on our public events and Friday lunchtime sessions. On our podcast interview, he shared how joining the Seminar’s interdisciplinary conversations about data and (reference intended!) information ecosystems has revealed the need for and rewards of approaching the same topics from distinct disciplinary and methodological viewpoints. And during our chat, I was alerted over and over to how valuable a historic approach to understanding science is. So often, we view STEM fields and workplaces as intrinsically separate from, and thus competing against, the humanities. This perceived divide has real-world consequences, among them the myths of STEM disciplines as ahistorical or apolitical, and the ultimately dangerous devaluing and underfunding of humanities programs. But Dr. Phillips’ work stands as a testament to the very real insights to be gained from a historical approach to math, science, statistics, and medicine. His current research focuses on the long histories of precision medicine and statistical approaches within. In the wake of the ongoing COVID-19 pandemic, the concept of precision medicine has come under renewed scrutiny. Precision medicine proposes that medical practices ranging from decisions, diagnoses, treatments, and products can be tailored to precise subgroups of patients—taking into account their genetics, environment, and lifestyle, rather than a “one size fits all” approach. For many Read More

Augmented Reality as a New Reality: How AR is Changing Monuments, Memorials, and Information Retrieval

2021-02-22
By: Jane Rohrer
On: February 22, 2021
In: Uncategorized
Tagged: anti-racism, archives, artificial intelligence, augmented reality, Big Data, black history month, history, racism, virtual reality

When you read the phrase “Augmented Reality,” your mind might turn to something like Pokémon GO or the popular running app, Zombies, Run! In both cases, the user experiences a game that, while based in a real-world environment, includes computer-generated perceptual information—most typically visuals and sounds, but including haptic modalities, too. A Pokémon, GO player might make their way down a very real hiking trail or across a downtown street while their phones display that very same location—the only difference being a virtual Growlithe waiting to be captured atop a tree stump or storm drain. Augmented Reality is a term that’s been in the mainstream public consciousness for decades now; for example, AR in the form of what’s known as Heads Up Display (HUD), which allows airplane pilots to read information on a clear glass screen atop the windshield itself (rather than a separate display), has been standard in aviation for decades now. But only very recently, alongside the rise of smartphones and Artificial Intelligence, has the true potentiality of AR become a mainstream, everyday reality—allowing it to flourish most popularly in entertainment, fitness, and marketing and commerce. Pokémon GO and Zombies, Run! have been around since 2016 and 2012 respectively, and in that time a whole world of Augmented Reality experiences have popped up. Alongside the video games and fitness experiences, there’s the Warby Parker app that allows users to virtually try on glasses, an IKEA app that places virtual furniture into users’ homes, and Snapchat filters that turned a Footlocker advertisement into a 3D Read More

Racism, Algorithms, and Blackness in Medicine: A Reading List for Black History Month During a Pandemic

2021-02-17
By: Jane Rohrer
On: February 17, 2021
In: Uncategorized
Tagged: Algorithms, Big Data, black history month, diversity, medical bias, medicine, racism

Happy Black History Month! The Seminar does not have any scheduled guests or podcasts so far this month, and so an opportunity arises to highlight voices & publications beyond our venerable (& growing!) list of participants. During this strange & stressful February, I wanted to make space, as SE (Shack) Hackney did last year, within Information Ecosystems to highlight some incredible and essential work by and about Black voices, and—amid a global pandemic—how race overlaps with medicine, data, and concepts of cure. What follows is an absolutely non-exhaustive reading list on topics of Blackness, medicine, data, and technology. I offer these pieces & voices as profoundly important to how we should be thinking about medicine and technology within our current moment; it is difficult to understate the debt we all owe to Black scholars, activists, scientists, doctors, and organizers, particularly in digitally-oriented spaces—but lending an eye or ear to their essential contributions is a start. And indeed, as the long shadow of COVID-19 extends toward its year-long mark, we must take seriously the disproportionally devastating impact the pandemic has had on our nation’s Black communities. Today, while the rate of hospitalization and death per 10,000 sits at 7.4 and 2.3 for white patients, it is a staggering 24.6 and 5.6 for Black patients (source). Scholars from a wide array of disciplines have over and over confirmed that the U.S. has a long and difficult history of racism in medicine. And, as our own Seminar guests—such as Dr. Safiya Noble and Dr. Sandra González-Bailón—have also confirmed, the Read More

Cartogram of the 2008 US Presidential Election results

Election Maps, Purple States, and Visualizing Space: A Visit with Professor Bill Rankin

2021-01-04
By: Jane Rohrer
On: January 4, 2021
In: Bill Rankin
Tagged: Big Data, Bill Rankin, cartography, Data, data visualization, election maps, maps

On Friday, December 4, The University of Pittsburgh’s Mellon-Sawyer Seminar Information Ecosystems: Creating Data (and Absence) from the Quantitative to the Digital Age was joined by Bill Rankin, an Associate Professor of the History of Science at Yale University. Professor Rankin’s research focuses on the relationship between science and mapping, the environmental sciences and technology, architecture and urbanism, in addition to methodological problems of digital scholarship, spatial history, and geographic analysis. His prize-winning first book, After the Map: Cartography, Navigation, and the Transformation of Territory in the Twentieth Century, was published by the University of Chicago Press in 2016. Professor Rankin is also an award-winning cartographer, and his maps have been published and exhibited widely in the U.S., Europe, and Asia. Rankin talked with the Sawyer Seminar Participants, who are faculty and students at the University of Pittsburgh and Carnegie-Mellon University, about cartography, election mapping, and the contemporary U.S. political landscape. Amid the many reactions to and characterizations of the historic 2020 Presidential election, this meeting helped the Seminar participants understand how and why election mapping continues to play an increasingly crucial role in the electoral process; in particular, Rankin’s talk touched generatively about the concept of “purple states” or “purple places.” Purple has been, in recent years, offered as a more representative complication to the simple binarism of “blue,” or liberal, and “red,” or conservative states. The “red” versus “blue” state discourse began as a simple, visual way for newscasters to characterize a state’s partisan tendencies over long durations of time. And while we do Read More

Data Pipelines, Data Fluidity: Colin Allen on the “Useful Fiction” of Curated Data

2020-02-28
By: Jane Rohrer
On: February 28, 2020
In: Colin Allen
Tagged: Big Data, Darwin, data pipelines, Topic modeling

Colin Allen, distinguished professor in the Department of History and Philosophy of Science at the University of Pittsburgh, is both an invited speaker and an ongoing participant in our Seminar; on February 28th, Dr. Allen talked with his fellow participants about his work in what he (and others) call “data pipelines.” Broadly speaking, using data pipelines means that data are collected and recorded in one of many particular ways—but eventually used for purposes other than why they were originally collected. And this means, Dr. Allen pointed out, that data are highly fluid, flexible, and even self-perpetuating. An especially potent example of this in Allen’s own work is his current role as Associate Editor of the Stanford Encyclopedia of Philosophy. While this project has one discreet start date back in 1995, it has been anything but static since then; as of March 2018, the site has approximately 1,600 entries each of which is routinely reviewed and updated. Each new post adds to what is now a highly dynamic reference work containing data culled from all over the web—a pipeline, indeed. Dr. Allen thoughtfully pointed out that as our relationship to data changes over our collective futures, it is important to remember that data does not enter into our world on its own but, rather, it is collected and curated. Allen co-authored an article, “Exploration and Exploitation of Victorian Science in Darwin’s Reading Notebooks,” with Jaimie Murdock and Simon DeDeo in 2017. Charles Darwin left careful records of the books he read from 1837 to 1860, making this Read More

The History of Science & Big Data’s Place in the Humanities

2019-11-15
By: Jane Rohrer
On: November 15, 2019
In: Sabina Leonelli
Tagged: Big Data, Data, Open Data, Philosophy of Science

The Sawyer Seminar’s November 15 guest was Dr. Sabina Leonelli. Dr. Leonelli teaches Philosophy and History of Science at the University of Exeter, where she is also the co-director of the Egenis Centre for the Study of Life Sciences. Her book Data-Centric Biology: A Philosophical Study, was published by the University of Chicago Press in 2016. She is now working on translating her 2018 book, Scientific Research in the Era of Big Data, into English from its original Italian. Both deal abundantly with the recent shifts and innovations in how researchers process and understand scientific data. In both her public talk on Thursday, November 14, and Sawyer Seminar lunch discussion, Dr. Leonelli walked us through the fundamentals of and distinctions between Big Data, Open Data, and FAIR Data (Findable, Accessible, Interoperable, Re-Usable); these distinctions—and mindful discussions about them—is increasingly necessary as, to quote Leonelli in Data-Centric Biology, “the rise of data centrism has brought new salience to the epistemological challenges involved in processes of data gathering, classification, and interpretation and…the social structures in which such processes are embedded” (2). As Leonelli described it, Big Data is definied by their capacity to move, be (re)used across situations & disciplines, and (re)aggregated into different useful and usable platforms. To elaborate here: while there is “no rigorous definition of Big Data,” we use them, in general, to complete large-scale projects that may not valuably be done at a smaller scale, often to extract new insights about an entire world, community, or issue. Humanist examples of this in practice would Read More

Invited Speakers

  • Annette Vee
  • Bill Rankin
  • Chris Gilliard
  • Christopher Phillips
  • Colin Allen
  • Edouard Machery
  • Jo Guldi
  • Lara Putnam
  • Lyneise Williams
  • Mario Khreiche
  • Matthew Edney
  • Matthew Jones
  • Matthew Lincoln
  • Melissa Finucane
  • Richard Marciano
  • Sabina Leonelli
  • Safiya Noble
  • Sandra González-Bailón
  • Ted Underwood
  • Uncategorized

Recent Posts

  • EdTech Automation and Learning Management
  • The Changing Face of Literacy in the 21st Century: Dr. Annette Vee Visits the Podcast
  • Dr. Lara Putnam Visits the Podcast: Web-Based Research, Political Organizing, and Getting to Know Our Neighbors
  • Chris Gilliard Visits the Podcast: Digital Redlining, Tech Policy, and What it Really Means to Have Privacy Online
  • Numbers Have History

Recent Comments

    Archives

    • June 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • October 2020
    • September 2020
    • May 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019

    Categories

    • Annette Vee
    • Bill Rankin
    • Chris Gilliard
    • Christopher Phillips
    • Colin Allen
    • Edouard Machery
    • Jo Guldi
    • Lara Putnam
    • Lyneise Williams
    • Mario Khreiche
    • Matthew Edney
    • Matthew Jones
    • Matthew Lincoln
    • Melissa Finucane
    • Richard Marciano
    • Sabina Leonelli
    • Safiya Noble
    • Sandra González-Bailón
    • Ted Underwood
    • Uncategorized

    Meta

    • Log in
    • Entries feed
    • Comments feed
    • WordPress.org

    Tags

    Algorithms Amazon archives artificial intelligence augmented reality automation Big Data Bill Rankin black history month burnout cartography Curation Darwin Data data pipelines data visualization digital humanities digitization diversity Education election maps history history of science Information Information Ecosystems Information Science Libraries LMS maps mechanization medical bias medicine Museums newspaper Open Data Philosophy of Science privacy racism risk social science solutions journalism Ted Underwood Topic modeling Uber virtual reality

    Menu

    • InfoEco Podcast
    • InfoEco Blog
    • InfoEco Cookbook
      • About
      • Curricular Pathways
      • Cookbook Modules

    Search This Site

    Search

    The Information Ecosystems Team 2023

    This site is part of Humanities Commons. Explore other sites on this network or register to build your own.
    Terms of ServicePrivacy PolicyGuidelines for Participation