actor-network theory: machines fdm 20c introduction to digital media lecture 29.04.2004

1. actor-network theory: machines fdm 20c introduction to digital media lecture 29.04.2004 warren sack / film & digital media department / university of california, santa cruz

2. the near future • next time: midterm • two weeks from today: version 1 of your paper is due

3. last time • latour’s first two “rules of method” • latour’s first principle

4. last time • latour’s rules of method are means to allow outsiders to follow science and technology • latour’s principles are summaries of large numbers of empiricial facts

5. the first two of latour’s rules of method • “We study science in action and not ready made science or technology; to do so, we either arrive before the facts and machines are blackboxed or we follow the controversies that reopen them.” (Introduction) • “To determine the objectivity or subjectivity of a claim, the efficiency or perfection of a mechanism, we do not look for their intrinsic qualities but at all the transformations they undergo later in the hands of others.” (Chapter 1)

6. principle: a definition • a summary of a large number of empiricial facts

7. latour’s first principle • “The fate of facts and machines is in later users’ hands; their qualities are thus a consequence, not a cause, of collective action.” (Chapter 1)

8. outline • a short history of latour’s approach: • narrative theory • rhetoric • a key point: Networks are not just a technology; they are also a means for understanding media; • some examples of network diagrams designed to explain specific media and mediations • latour’s definition... • of a machine; • of an automaton; and, • his refined definition of black box • latour’s explanation of translation...versus what is commonly called “diffusion” • a third principle & fourth rule of method

9. where does latour’s approach come from? • source one: the humanities, specifically, narrative theory • question: who, or what, can be a character in a story? what kinds of character can appear in a story? • vladimir propp, morphology of the fairy tale • propp was a russian formalist in the 1920s • algirdas greimas, structural semantics • greimas was a “french” structuralist in the 1960s

10. where does latour’s approach come from? • source two: the humanities, specifically, rhetoric • question: how can someone or something best represent or stand-in for someone or something else?

11. tropes of rhetoric • how do we make one thing, or person, represent something more, less, or other? • metaphor • a figure of speech in which an expression is used to refer to something that it does not literally denote in order to suggest a similarity • metonymy • a figure of speech in which an attribute is substituted for the whole • synecdoche • A figure by which a more comprehensive term is used for a less comprehensive or vice versa; as whole for part or part for whole, genus for species or species for genus • irony • A figure of speech in which the intended meaning is the opposite of that expressed by the words used; usually taking the form of sarcasm or ridicule

12. key point • Networks are not just a technology; they are also a means for understanding media. • cf., actor-network theory; social network analysis; etc. • heterogeneous networks provide us with a way to show how people and things are associated together and/or stand-in for one another

13. example: text as network as hypertext from geneviéve teil & bruno latour, “the hume machine: can association networks do more than formal rules?”, stanford humanities review, volume 4, issue 2

14. example: text network 1

15. example: text network 2

16. example: text network 3

17. example: text networks 1 + 2 + 3

18. example: networks of people (i.e., social networks) from “Studying Online Social Networks” by Laura Garton, Caroline Haythornthwaite and Barry Wellman, Journal of Computer- Mediated Communication, Vol. 3, No. 1., June 1997 social network before the introduction of a CMC system

19. example: networks of people (i.e., social networks) from “Studying Online Social Networks” by Laura Garton, Caroline Haythornthwaite and Barry Wellman, Journal of Computer- Mediated Communication, Vol. 3, No. 1., June 1997 social network six months after the introduction of a CMC system

20. example: networks of people and articles (e.g., citation and co-authorship networks) this image is from the system ReferalWeb by Henry Katz et al. at ATT Research http://foraker.research.att.com/refweb/version2/RefWeb.html

21. example: networks of words and people from Warren Sack, Conversation Map, www.sims.berkeley.edu/~sack/cm/

22. example: networks of technologies(i.e, technical networks; e.g., computer networks)

23. networks of people and technologies • open source software browser: visualizing large, public collaborations: how do open source software communities reproduce themselves? • nicolas ducheneaut, ph.d. dissertation, uc berkeley sims, may 2003

25. hybridization (of people and technologies) in action, ducheneaut (2003)

26. translation in action ducheneaut (2003) nicolas ducheneat, 2003

27. how does someone get their ideas accepted? • latour’s answer: translation • five kinds of translation: • piggybacking: i want what you want. • seduction: i want it, why don’t you? • detour: convince them they are cut off. • reshuffling • displacing goals • inventing new goals • inventing new groups • drift: rendering the detour invisible • becoming indispensible...i.e., make a machine!

28. translation 1: piggybacking: i want what you want

29. translation 2: seduction: i want it, why don’t you?

30. translation 3: detour:if you just make a short detour...

31. translation 4: reshuffling

32. translation 4: tactics • displacing goals • inventing new goals • inventing new groups • drift: rendering the detour invisible

33. translation 5: become indispensible...make a machine

34. what is a machine? • machine: A machine, as its name implies, is first of all, a machination, a stratagem, a kind of cunning, where borrowed forces keep one another in check so that none can fly apart from the group.

35. machine v. tool v. medium • machine: A machine, as its name implies, is first of all, a machination, a stratagem, a kind of cunning, where borrowed forces keep one another in check so that none can fly apart from the group. • tool: This makes a machine different from a tool which is a single element held directly in the hand of a man or a woman. • Latour, Science in Action, p. 129 • medium: A medium is a material, device, or process that holds people together or separates them apart from one another. I.e., a medium is a machine in Latour’s terms.

36. automaton: a definition • machines in which the assembled forces move by themselves (p. 130)

37. black boxes: a refined definition • black box = automaton • “Until it can be made into an automaton, the elements that the fact-builder wants to spread in time and space is not a black box.” (p. 131)

38. science v. technology • science and technology can only be distinguished by two moments: • “The first moment is when new and unexpected allies are recruited -- and this is most often visible in laboratories, in scientific and technical literature, in heated discussions; • the second moment is when all the gathered resources are made to act as one unbreakable whole -- and this is more often visible in engines, machines and pieces of hardware.” (pp. 131-132)

39. black boxes are a result of knitting together humans and non-humans (p. 139)

40. unraveling the sociotechnograms • think forward to the future: e.g., imagine how well your computer will work in 20 years, even if it still “runs” • think backward to the past; imagine what your computer was like while it was still on the drawing board, being designed.

41. latour’s fourth rule of method • Since the settlement of a controversy is the cause of Society’s stability, we cannot use Society to explain how and why a controversy has been settled. We should consider symmetrically the efforts to enrol human and non-human resources.

42. latour’s third principle • We are never confronted with science, technology and society, but with a gamut of weaker and stronger associations; thus understanding what facts and machines are is the same task as understanding who the people are.

43. next time • midterm • remember the possible questions for the midterm can be found on the course website: dmedia.ucsc.edu/FDM20c/Winter2005/Midterm/possible-questions.doc