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What isa border_kings

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A conference I gave at the Kings's College doctoral school with Mathieu Jacomy on the notion of social border and the advantage of adding continuity in social research through digital navigation.

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What isa border_kings

  1. 1. What is a Boundary? On Continuity and Density in the Social Sciences Tommaso Venturini
  2. 2. Follow the White Rabbit why controversy mapping (and digital methods) will change everything you know about sociology Tommaso Venturini tommaso.venturini@sciences-po.fr The strabismus of social sciences Photo credit – tarout_sun via Flickr - ©
  3. 3. 3 discontinuities • 1. In data: intensive data / extensive data • 2. In methods: situating / aggregating • 3. In theory: micro-interactions / macro-structure
  4. 4. Part I Data: intensive / extensive
  5. 5. The quali/quantitative divide poor data on large population extensive data intensive data rich data on small population
  6. 6. Extensive data Paul Butler, 2010 Visualizing Friendships
  7. 7. Intensive data AOL user 711391 search history www.minimovies.org/documentaires/view/ilovealaska
  8. 8. Extensive and intensive data Google Flu www.google.org/flutrends
  9. 9. Extensive and intensive data Google Flu www.google.org/flutrends
  10. 10. Extensive and intensive data Venturini, Tommaso and Bruno Latour, 2010 “The Social Fabric: Digital Traces and Quali-Quantitative Methods” in Proceedings of Future En Seine 2009, pp. 87–101 Paris: Editions Future en Seine.
  11. 11. This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves. Chris Anderson http://www.wired.com/science/discoveries/ magazine/16-07/pb_theory The end of theory?
  12. 12. Askitas, N., & Zimmermann, K. 2011 Health and Well-Being in the Crisis IZA Discussion Paper Beware: digital data is not your data!
  13. 13. Beware: digital data is not your data!
  14. 14. http://googlesystem.blogspot.fr/2008/08/go ogle-suggest-enabled-by-default.html Beware: digital data is not your data!
  15. 15. Part II Methods: situating / aggregating
  16. 16. (Collective) life is complicated Andreas Gursky 1999 Chicago, Board of Trade II
  17. 17. Situating VS aggregating Armin Linke Inside / Outside
  18. 18. La fabrique de la loi http://www.lafabriquedelaloi.fr
  19. 19. Extensive and intensive data Latour, Bruno, Pablo Jensen, Tommaso Venturini, Sébastian Grauwin and Dominique Boullier, 2012. “‘The Whole Is Always Smaller than Its Parts’: A Digital Test of Gabriel Tardes’ Monads.” The British journal of sociology 63(4), pp. 590–615
  20. 20. Part III Theory: micro-interactions / macro-structure
  21. 21. The micro/macro boundary Merian & Jonston 1718 Folio Ants, Clony, Nest, Insects Thomas Hobbes, 1651 The Leviathan
  22. 22. An ontological and emergent boundary The collective self is not a simple epiphenomenon of its morphologic base, precisely as the individual self is not a simple efflorescence of the nervous system. For the collective self to appear, a sui generis synthesis of individual self has to be produced. This synthesis creates a world of feelings, ideas, images that, once come to life, follow their own laws. Emile Durkheim, 1912 Le formes élémentaires de la vie religieuse
  23. 23. …that may hide other more relevant boundaries zgrossbart.github.io/hbo ecycling/
  24. 24. From boundaries to boundary work Fences make good neighbors Gieryn, Thomas F. (1983) Boundary-work the demarcation of science from non-science American Sociological Review 48(6): 781–795 Demarcation is as much a practical problem for scientists as an analytical problem for sociologists and philosophers
  25. 25. The lesson of ANT (and of constructivism) It is not that in collective life there are no boundaries (between micro and macro, science and politics…), It is that all boundaries are constantly constructed, de-constructed and re-constructed (and this is work is the object of social research)
  26. 26. The lesson of ANT (and of constructivism) It is not that in collective life there are no boundaries (between micro and macro, science and politics…), It is that all boundaries are constantly constructed, de-constructed and re-constructed (and this is work is the object of social research) Venturini, T. (2010). Diving in magma: how to explore controversies with actor-network theory. in Public Understanding of Science, 19(3), 258–273.
  27. 27. Part IV Becoming sensitive to the differences in the density of association
  28. 28. 3 discontinuities • 1. In data: intensive data / extensive data • 2. In methods: situating / aggregating • 3. In theory: micro-interactions / macro-structure
  29. 29. 3 discontinuities to cross • 1. In data: intensive data / extensive data Digital traceability and computation (data geeks) • 2. In methods: situating / aggregating Datascape navigation (designers) • 3. In theory: micro-interactions / macro-structure A non-emergentist theory of action (actor-network theorists)
  30. 30. A network (graph) is not a network (actor-network)
  31. 31. A network (graph) is not a network (actor-network) Actor-Network Theory Visual Network Analysis Actors and networks have the same properties (they are the same) ≠ Networks are composite while nodes are indivisible and uncombinable Different mediations (can) have different effects ≠ All edges have the same effect (possibly with different weight) Different actors (can) have different association potential ≠ All nodes have equal linking potential A-N are always seen from one or more specific viewpoints ≠ Networks are usually seen from above/outside What counts is change ≠ Networks are statics
  32. 32. A question of resonance A diagram of a network, then, does not look like a network but maintain the same qualities of relations – proximities, degrees of separation, and so forth – that a network also requires in order to form. Resemblance should here be considered a resonating rather than a hierarchy (a form) that arranges signifiers and signified within a sign (p. 24). Munster, A. (2013). An Aesthesia of Networks Cambridge Mass.: MIT Press
  33. 33. The fabric of (cooked) rice Roland Barthes (1970) The Empire of Signs Cooked rice (whose absolutely special identity is attested by a special name, which is not that of raw rice) can be defined only by a contradiction of substance; it is at once cohesive and detachable; its substantial destination is the fragment, the clump; the volatile conglomerate… it constitutes in the picture a compact whiteness, granular (contrary to that of our bread) and yet friable: what comes to the table to the table, dense and stuck together, comes undone at a touch of the chopsticks, though without ever scattering, as if division occurred only to produce still another irreducible cohesion (pp. 12-14).
  34. 34. The fabric of collective life Jacob L. Moreno, April 3, 1933 The New York Times Social life is continuous but not homogenous Doing social research is becoming sensitive to the differences in the density of association
  35. 35. Force-vector algorithms
  36. 36. Force-vectors’ magic trick
  37. 37. Force-vectors’ magic trick Jacomy, M., Venturini, T., Heymann, S. & Bastian, M. (2014) ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PlosONE, 9:6
  38. 38. Network as maps London Underground 1920 map homepage.ntlworld.com/clivebillson/tube/tube.html - www.fourthway.co.uk/tfl.html
  39. 39. Network as maps London Underground 1933 map (Harry Beck) homepage.ntlworld.com/clivebillson/tube/tube.html - www.fourthway.co.uk/tfl.html
  40. 40. Part V Visual Network Analysis
  41. 41. Semiology of graphics Bertin J., Sémiologie graphique, Paris, Mouton/Gauthier-Villars, 1967
  42. 42. Visual variables A B C
  43. 43. Visual network analysis questions A. Position (force-vector spatialization) 1. Nodes density Where are structural holes (under-populated regions)? Where are clusters an sub-clusters (over-populated regions)? Which are the largest and most cohesive clusters? 2. Relative position Which nodes/clusters are globally and locally central? Which nodes/clusters are global and local bridges (between clusters)? B. Size (ranking by in-degree / out-degree) 3. Nodes connectivity Which nodes are the authorities (receive most connections)? Which nodes are the hub (originate most connections)? C. Color (color by partition) 4. Distribution Is typology coherent with topology (partitions coincide with clusters)? Which are the exceptions (‘misplaced nodes’)?
  44. 44. Visual network analysis questions A. Position (force-vector spatialization) 1. Nodes density Where are structural holes (under-populated regions)? Where are clusters an sub-clusters (over-populated regions)? Which are the largest and most cohesive clusters? 2. Relative position Which nodes/clusters are globally and locally central? Which nodes/clusters are global and local bridges (between clusters)? B. Size (ranking by in-degree / out-degree) 3. Nodes connectivity Which nodes are the authorities (receive most connections)? Which nodes are the hub (originate most connections)? C. Color (color by partition) 4. Distribution Is typology coherent with topology (partitions coincide with clusters)? Which are the exceptions (‘misplaced nodes’)?
  45. 45. Main cluster and structural holes
  46. 46. Sub-clusters
  47. 47. Modularity
  48. 48. Visual network analysis questions A. Position (force-vector spatialization) 1. Nodes density Where are structural holes (under-populated regions)? Where are clusters an sub-clusters (over-populated regions)? Which are the largest and most cohesive clusters? 2. Relative position Which nodes/clusters are globally and locally central? Which nodes/clusters are global and local bridges (between clusters)? B. Size (ranking by in-degree / out-degree) 3. Nodes connectivity Which nodes are the authorities (receive most connections)? Which nodes are the hub (originate most connections)? C. Color (color by partition) 4. Distribution Is typology coherent with topology (partitions coincide with clusters)? Which are the exceptions (‘misplaced nodes’)?
  49. 49. Central nodes and clusters
  50. 50. Bridging nodes and clusters
  51. 51. Visual network analysis questions A. Position (force-vector spatialization) 1. Nodes density Where are structural holes (under-populated regions)? Where are clusters an sub-clusters (over-populated regions)? Which are the largest and most cohesive clusters? 2. Relative position Which nodes/clusters are globally and locally central? Which nodes/clusters are global and local bridges (between clusters)? B. Size (ranking by in-degree / out-degree) 3. Nodes connectivity Which nodes are the authorities (receive most connections)? Which nodes are the hub (originate most connections)? C. Color (color by partition) 4. Distribution Is typology coherent with topology (partitions coincide with clusters)? Which are the exceptions (‘misplaced nodes’)?
  52. 52. Authorities
  53. 53. Hubs
  54. 54. Visual network analysis questions A. Position (force-vector spatialization) 1. Nodes density Where are structural holes (under-populated regions)? Where are clusters an sub-clusters (over-populated regions)? Which are the largest and most cohesive clusters? 2. Relative position Which nodes/clusters are globally and locally central? Which nodes/clusters are global and local bridges (between clusters)? B. Size (ranking by in-degree / out-degree) 3. Nodes connectivity Which nodes are the authorities (receive most connections)? Which nodes are the hub (originate most connections)? C. Color (color by partition) 4. Distribution Is typology coherent with topology (partitions coincide with clusters)? Which are the exceptions (‘misplaced nodes’)?
  55. 55. Typology and topology
  56. 56. Typology and topology
  57. 57. Exceptions
  58. 58. Visual network analysis
  59. 59. Visual network analysis Venturini, T., Jacomy, M and De Carvalho Pereira, D. Visual Network Analysis: The example of the rio+20 online debate (working paper)
  60. 60. http://www.tommasoventurini.it/

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