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The Network Structure of Sociology Production

The Network Structure of Sociology Production. James Moody Ohio State University Indiana University December, 2005. Introduction. Outline: Big Picture: Networks, Structure, Action & Outcomes Guiding Questions & General Approach Examples: Hierarchy, Romance & the spread of STDs

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The Network Structure of Sociology Production

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  1. The Network Structure of Sociology Production James Moody Ohio State University Indiana University December, 2005

  2. Introduction • Outline: • Big Picture: Networks, Structure, Action & Outcomes • Guiding Questions & General Approach • Examples: Hierarchy, Romance & the spread of STDs • Networks & Science: Two Questions & 4 networks • How do scientific fields evolve? • Where do good ideas come from? • Data Sources & Methods • Results • Where does sociology fit? Journal co-citation networks • What do sociologists study? Topic networks • Who produces sociology? Social science collaboration networks • Discussion

  3. Networks, Structure, Action & Outcomes Guiding Questions: • Where does social structure come from? • How does social structure enable & constrain action & outcomes? • General Approach: • (1) Seek structure in patterns of association: “To speak of social life is to speak of the association between people – their associating in work and in play, in love and in war, to trade or to worship, to help or to hinder. It is in the social relations men establish that their interests find expression and their desires become realized.” — Peter M. Blau Exchange and Power in Social Life, 1964

  4. Networks, Structure, Action & Outcomes Guiding Questions: • Where does social structure come from? • How does social structure enable & constrain action & outcomes? • General Approach: • (2) Focus on large-scale network structure: "If we ever get to the point of charting a whole city or a whole nation, we would have … a picture of a vast solar system of intangible structures, powerfully influencing conduct, as gravitation does in space. Such an invisible structure underlies society and has its influence in determining the conduct of society as a whole." — J.L. Moreno, New York Times, April 13, 1933

  5. Networks, Structure, Action & Outcomes Guiding Questions: • Where does social structure come from? • How does social structure enable & constrain action & outcomes? • General Approach: • (3) Link well-defined network structures to relevant social theory… “The social structure [of the dyad] rests immediately on the one and on the other of the two, and the secession of either would destroy the whole. . . . As soon, however, as there is a sociation of three, a group continues to exist even in case one of the members drops out.” —Simmel ([1908] 1950:123) This can then be operationalized as node-connectivity directly.

  6. Networks, Structure, Action & Outcomes Guiding Questions: • Where does social structure come from? • How does social structure enable & constrain action & outcomes? • General Approach: • (4) …in a manner that can explain truly emergent social properties. “[Social facts] assume a shape, a tangible form peculiar to them and constitute a reality sui generis vastly distinct from the individual facts which manifest that reality” — Durkheim Rules Of Sociological Method

  7. Networks, Structure, Action & Outcomes Examples: Hierarchy in High School A Gallery of Friendship Networks 776 adolescents from a working-class, all-white, suburban, school in the Midwest. (Source: Add Health)

  8. Networks, Structure, Action & Outcomes Examples: Hierarchy in High School A Gallery of Friendship Networks 678 adolescents from a working-class, all-white, rural, school in the Midwest. Across these settings (and many more) we can literally see the differences imposed by classic ‘Blau space’ features of youth communities. Race, grades, SES etc. often shape the gross topography of school friendship networks. (Source: Add Health)

  9. Networks, Structure, Action & Outcomes Examples: Hierarchy in High School Distribution of Popularity Size Community type By size and city type

  10. Networks, Structure, Action & Outcomes • If you examine all schools you find: • All of the school networks have a rank-strata structure • The structure remains constant even though nearly half of all relationships are new • People’s position in the popularity distribution is fluid • What social process will explain a stable macro-structure in the face of dynamic relations?

  11. Networks, Structure, Action & Outcomes Examples: Hierarchy in High School Endogenous Building Blocks: A periodic table of social elements: (0) (1) (2) (3) (4) (5) (6) 003 012 102 111D 201 210 300 021D 111U 120D 021U 030T 120U 021C 030C 120C

  12. Intransitive Transitive Mixed Networks, Structure, Action & Outcomes Examples: Hierarchy in High School • Classic balance theory offers a set of simple local rules for relational change: • A friend of a friend is a friend • My enemy’s enemy is my friend. (0) (1) (2) (3) (4) (5) (6) 003 012 102 111D 201 210 300 021D 111U 120D 021U 030T 120U 021C 030C 120C

  13. vacuous transition Increases # transitive Decreases # intransitive Decreases # transitive Increases # intransitive Vacuous triad Intransitive triad Transitive triad Networks, Structure, Action & Outcomes Examples: Hierarchy in High School 030C 120C 102 111U 021C 201 012 300 111D 003 210 021D 120U 030T 021U 120D (some transitions will both increase transitivity & decrease intransitivity – the effects are independent – they are colored here for net balance)

  14. Transitivity Intransitivity Reciprocity Networks, Structure, Action & Outcomes Examples: Hierarchy in High School ERGM Coefficient Distributions* 0.8 Endogenous Focal Orgs. Dyadic Similarity/Distance. 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 GPA SES Fight College Drinking Same Sex Same Race Both Smoke Same Clubs Same Grade *Coefficients based on pseudo-likelihood approximations, here standardized so they fit well on the page…

  15. Networks, Structure, Action & Outcomes Examples: Building Romantic Networks

  16. Networks, Structure, Action & Outcomes Examples: Building Romantic Networks

  17. Networks, Structure, Action & Outcomes Examples: Building Romantic Networks What micro-structures are taboo in high-school romantic relations?

  18. Networks, Structure, Action & Outcomes Examples: Building Romantic Networks The 4-cycle prohibition fits the observed data.

  19. Networks, Structure, Action & Outcomes Examples: Systemic Effects of Local Action in STD Cores • An STD Puzzle: • contact rates are low (most people have few partners) • dyadic transmission is difficult (compared, say, to the flu) • people are infectious for short periods of time • Particularly for bacterial STDs, but even AIDS infectiousness peaks shortly after acquiring the disease • How does the disease manage to remain endemic? • Activity heterogeneity is the common answer: a few active “stars” keep the disease endemic • But this doesn’t fit the empirical facts-on-the-ground in many cases. • What if many people make small changes, instead of few people making big changes?

  20. Networks, Structure, Action & Outcomes Examples: Systemic Effects of Local Action in STD Cores

  21. Networks, Structure, Action & Outcomes Examples: Systemic Effects of Local Action in STD Cores

  22. Networks, Structure, Action & Outcomes • While my substantive work has ranged widely, I always focus on the intersection of individual action embedded in network structures over time. • The long-term goal is to identify fundamental principles for either networks or action that can explain the wide variety observed social structures with a small number of locally digestible and contextually relevant action rules. • My new work turns these tools to questions about the development of science.

  23. Networks & Science: Two Questions & 4 networks • How do scientific fields evolve? • Is there a coherent logic to the ebb and flow of topics studied? • How does the success or failure of ideas depend on the social community in which it is embedded? • (How) Does the evidentiary basis of a field shape it’s logic of discovery? • The descriptive answer is given by mapping the field in network space. • The analytic answer will come by modeling the emergence, growth and decline of scientific subfields.

  24. Networks & Science: Two Questions & 4 networks • 2) Where do good ideas come from? • What is a good idea? • Ideas that change a scientific field. Indexed by (a) citations and (b) the relevant topography of the networks within which the idea was originally embedded. Ideas are not inherently good; they are recognized as “good” by their effect on a field. • How do disciplines produce new ideas? • Intersection Good ideas are produced by combining ideas of others in unique ways (Burt) • Development  Good ideas arise naturally from either the progressive “error reduction” process of good normal science (Popper) or the accepted practices of a scientific community (Crane). • Peer Influence & Recognition  Any idea is a good idea if others think so, and thinking so is influenced by the network. (Gould). • Resource competition  Search for prestige conditioned by organizational structure (Fuchs) • Will model this by examining how citations are affected by field dynamics (and vice versa).

  25. Networks & Science: Two Questions & 4 networks Theoretical approaches to scientific development We are thus left with multiple action frames to guide our understanding: Truth: Ideas run their error-reduction course (Popper) Prestige: Actors seek the greatest visibility (Merton) Resource competition: “To the victor goes the spoils” – Fuchs Boundary Protection ( Lamont) Fractal Development (Abbott) Community Influence (SSK – Collins, etc) Peer magnification (Gould) Power (JL Martin) For entire fields, these mechanisms are largely unknown and underspecified.  Need to extend beyond particular lab studies  Take a large-scale “Satellite” view of science dynamics  Link action frames to specific patterns in 4 science networks

  26. Networks & Science: Two Questions & 4 networks Theoretical approaches to scientific development • Four relevant networks: • Citation networks – a direct trace of scientific recognition & production • Topic networks – clusters of scientific products related to the same subject • Collaboration networks – “invisible communities” of social interaction that produces scientific products • Research Communities – People linked through common research topics (Substantively a derivative of 2 & 3)

  27. Networks & Science: Two Questions & 4 networks Scientific Environments Evidentiary Basis: How do we array disciplines with respect to evidence? Two Dimensions: Objectivity & Control Objectivity is taken from Popper: The extent to which a given knowledge claim is independent of the knower. Control refers to the ability of scientists to directly manipulate the object of study. “Lab Science” with complete ability to control apparatus (and thus environment) represents the strongest ability, while “observation” represents the other. Cases: Chemistry (Lab Science: High Objectivity & High Control) Geology (Field Science: High Objectivity & Low Control) Sociology (Social Science: Moderate Objectivity & Low Control) Literary Criticism (Humanities: Low Objectivity & Low Control) This approach is very similar to Fuchs (1993)

  28. Networks & Science: Two Questions & 4 networks

  29. Networks & Science: Two Questions & 4 networks Focusing on Sociology as a current case • The field of sociology can thus be thought of as the intersection of multiple networks. • The shape of these networks differs across scales and over time. • - Differences between local and global visions of the network shape our perceptions of scientific coherence. • We tend to perceive coherence in our own specialty fields and incoherence for the entire discipline. • A globally federated structure, that cannot easily exclude empirical topics, might still be socially coherent if scientific mixing cross-cuts empirical problems. • We can see this structure by examining these 4 networks at large scale and over time.

  30. Data Sources • Citation Networks • Compiled from the ISI web of science Journal citation tables • Covers 1681 social science journals indexed in 2003 • Will eventually • -fill this series from 1990 to present across all fields. • -Add a sample of paper-level citations to model performance. • Topic & Collaboration Networks (for Sociology) • Compiled from Sociological Abstracts • 281,163 papers published between 1963 and 1999 • A sub-sample of “sociology only” papers published in a select set of non-specialty sociology journals  35% of the total (~100K) • Contains information on title, abstract, keywords, author(s), tables, journal & citation • Will use similar indexes for Chemistry, Geology and Lit Crit

  31. 50 Number of ASA Sections 45 40 35 30 25 20 15 10 5 0 1950 1960 1970 1980 1990 2000 2010 Where does sociology fit? • Perennial debates over the existence of a theoretical core • Rapid growth in the internal diversity of topics sociologist study:

  32. Where does sociology fit? • Perennial debates over the existence of a theoretical core • Rapid growth in the number of journals relevant to sociologists:

  33. Where does sociology fit? • This growth & diversity has been seen as evidence for the ultimate emptiness of sociology as a scientific discipline. • But disciplines are shaped by the connections between ideas, not the number of ideas. • That is, we recognize fields by who they speak to as much as by what they speak about. • The clearest empirical trace of this communication is citation. • Disciplines can then be defined as clusters of work that speak more to each other than to anyone else, which we trace with co-citation networks.

  34. AJS ASR AER … JER J1 J2 J3 J4 . . . JER # # 0 0 0 0 # # # 0 # 0 # 0 # # 0 # 0 # Where does sociology fit? Building co-citation networks Links in a co-citation network are constructed by measuring how similar each journal is to every other journal. Similarity is gauged by correlating the pattern of citations received by each journals from every other journal. Comparing across columns tells us whether the two journals are recognized by others as similar.

  35. Where does sociology fit? Building co-citation networks AJS ASR AER … JER AJS ASR AER . . . JER Links in a co-citation network are constructed by measuring how similar each journal is to every other journal. Similarity is gauged by correlating the pattern of citations received by each journals from every other journal. 1.0 High 1.0 1.0 Low Med Low High Low 1.0 This create a valued network of ties between two journals. I use a cosine similarity score developed in bibliometrics, selected for those with ties > 0.45 & at sharing at least 2% of their citation volume. Source: Loet Leydesdorff

  36. Where does sociology fit? Economics co-citation similarity network Density = 0.197 N=152 Isolates (not shown): 5 Node size proportional to log(degree)

  37. Where does sociology fit? Political Science co-citation similarity network Density = 0.160 N=69 Isolates (not shown): 10 Node size proportional to log(degree)

  38. Where does sociology fit? Sociology co-citation similarity network Density = 0.140 N=69 Isolates : 7

  39. Where does sociology fit?

  40. Where does sociology fit?

  41. Where does sociology fit?

  42. Where does sociology fit? • Sociology “fits” at the center of the social sciences. We are not as internally cohesive as Economics or Law, but more so than many (anthropology, allied health fields). • This represents a tradeoff. We have traded unique dominance of a topic (markets, politics, mind, space, history) for diversity & thus centrality. • Sociology is an interstitial discipline (Abbott, 2004) in at least two-senses: • There is no content topic we can reasonably exclude • We pull together, and generate, the ideas and topics covered by specialty disciplines. • This makes us uniquely positioned to provide insights on many different empirical questions. How have the topics sociologists study shifted over time?

  43. What do sociologists study? • How do we capture the internal organization of research problems? • Could use paper-level citation networks (see Hargens 2000), but data are difficult & expensive to obtain for large-scale networks. • Can examine the network of papers formed by the topics they write about. • Directly taps scientific content • Purely endogenous creation of topics that allows new topic areas to emerge and old ones to die over time • Tractability: data can be extracted from information held in Sociological Abstracts • Multiple levels: • Coarse grained Focus solely on keywords (Light 2005) • Fine grained  Use all information available (title, abstract, keywords)

  44. What do sociologists study? A fine grained view • Data Selection & Manipulation: • Index entries contain title, abstract and keywords that summarize the paper’s content. • Sample all papers indexed within four 3-year windows between 1970 and 1999. • Construct a paper – by – word matrix, where the ij cell lists how many times word i is used to describe paper j. • Word set is stemmed to get at root words • A stop-list is used to minimize inclusion of low-information content words (“the” “and” “is” etc.) or words commonly found in the data source (“Tables” “Figure” “References”) • Construct a network by linking the most highly correlated papers • Use correlation of 0.40 or better • Ties are treated as valued in the network analyses

  45. What do sociologists study? A fine grained view • Analysis & Presentation: General approach is “quantitatively inductive” • - Construct a low-dimensional map of the network, using contour sociograms. These allow for full information in the network structure. • Use cluster analysis to identify distinct topics • Use a variant of Moody’s RNM algorithm to cluster the network • This clustering routine: • (a) is efficient: Allows clustering on 10s of thousands of nodes • (b) automatically specifies the optimal number of clusters • (c) allows that some cases can fall ‘between’ clusters • I set a minimum cluster size of 12 papers published over the 3-year window. • Evaluate the clustered papers for content and label the maps.

  46. What do sociologists study? A fine grained view Analysis & Presentation: General approach is “quantitatively inductive” Compare the maps over time qualitatively, looking for general changes in the frequency & alliance of topics. Examine shifts in structural indicators of the extent of clustering & cluster size distributions.

  47. What do sociologists study? A fine grained view Example: One-step neighborhood of “More information, better jobs?”

  48. What do sociologists study? A fine grained view Example: One-step neighborhood of “More information, better jobs?”

  49. What do sociologists study? A fine grained view: Content (all journals)

  50. What do sociologists study? A fine grained view: Content (all journals)

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