Day Three: Big Data Discourse

SDS 237: Data Ethnography

Lindsay Poirier

Data is the new ________. (Fill with a term that represents dominant beliefs about data.)

Turn to a neighbor and discuss:

  • What assumptions are built into this metaphor?
  • What are the some social consequences of associating data with this idea?

What did we learn about epistemology on Tuesday? How is this relevant to big data?

Feminist Epistemologies

  • All knowledge is embodied
    • Contrast with disembodied knowledge - i.e. not tied to a specific body
  • Bodies are situated in certain social positions and have a finite point of view (Haraway 1991)
    • Critique of the “unmarked body,” the “God trick,” or the “view from nowhere”
  • Knowledge is tied to particular standpoints
    • Our experiences, what we’ve read, our education, our social positions, and what our bodies enable us to do, see, hear, taste, touch, and smell
    • Factors are innumerable and unique to every person

marsroverdriver, CC BY-SA 2.0 https://creativecommons.org/licenses/by-sa/2.0, via Wikimedia Commons

Discourse

  • How we communicate or converse about topics, people, and things
  • Dominant discourse characterizes the discourses that emerge as predominant throughout society
    • Shapes our values, identities, behaviors, and interactions with each other
    • Also shapes, disseminates, and is prodded by our ideologies, or worldviews
  • Cultural hegemony describes when our ideologies reflect those with power over us

What social institutions shape predominant ways we talk about things?

What are some examples of dominant discourse?

Technology Discourse

  • Technocratic: Technology will fix social problems.
    • Computers will save the world!
    • Other examples?
  • Dystopian: Technology is frightening or debilitating.
    • Robots will take over all jobs.
    • Other examples?
  • Determinist: Technology determines how society operates.
    • Mobile phones are making us anti-social.
      • Other examples?

Discourse Analysis in Nine Steps

  1. Establish the context
  2. Consider the medium
  3. Discern the intended audience
  4. Assess assumptions
  5. Identify cultural cues and references
  6. Evaluate rhetorical strategies and methods of delivery
  7. Consider the social structures the discourse operates within
  8. Assess how the discourse disseminates
  9. Reflect on what is not said or who is not included

What connotations are wrapped into the metaphors often used to characterize data? Why do the metaphors we use to describe data matter?

Pull out a piece of paper, and draw a line down the center. On the left side, list adjectives that people use to describe “good” data. On the right side, write the opposite of each word you wrote on the left side.

Turn to your neighbor and discuss:

  • What data discourses are the words you wrote on the left side embedded within?
  • Can you identify any terms that might fit in between these opposites?

Binary Oppositions

  • Looking at the world through pairs of terms that we consider to have the opposite meaning
  • Examples include:
    • Real/fake
    • Objective/subjective
    • Nature/culture
  • Binary oppositions are reductionist, or oversimplify complexity
  • Binary oppositions are rooted in ideologies and disseminated through discourse

Hierarchies in Binary Oppositions

  • In dominant discourse, one half of a binary opposition tends to be positioned as superior than the other
  • One half tends to get treated as normal or pure, and other as a deviation from the normal, or tainted
  • Binary oppositions can reinforce privilege
  • What are some examples of some hiearchical binary oppositions?

Nature/Culture

  • Countless domains (disciplines, newspaper headings, etc.) organized around the divisions between nature and culture
  • Nature is often associated with purity, innateness, biology, or rawness.
  • Culture is seen as ‘Other’ to what is natural
    • e.g. human judgments bias science and decision-making
    • e.g. human cultures destroy the Earth’s purity
  • Feminist critiques:
    • Shows how purity is political
    • Argues that we can’t tell where nature stops and culture starts
    • Shows how the divisions justify treating certain social groups as superior and others as inferior
    • Refers to natureculture: hybrids reverse the logic of binary oppositions

What are some of the discursive risks of talking about raw data?

Good Bad
Raw Cooked
Clean Dirty
Objective Subjective
Transparent Opaque
Rigid Loose
Neat Scruffy
Neutral Partial
Scientific Political
Certain Uncertain
Observed Interpreted
Real Constructed
Unbiased Biased
Accurate Inaccurate

More on Deloitte’s Evergreen

Reminders

  • Be sure to record your work in the labor log!
  • Let me know if you’d like to lead a classroom reading discussion
  • Tonight’s Debate