Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate

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Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate

  1. 1. Reproduction of Hierarchy?A Social Network Analysis of the American Law Professoriate Daniel Martin Katz Josh Gubler Jon Zelner Michael Bommarito Eric Provins Eitan Ingall
  2. 2. Motivation for Project
  3. 3. Motivation for ProjectWhy Do Certain Paradigms, Histories, Ideas Succeed?
  4. 4. Motivation for ProjectWhy Do Certain Paradigms, Histories, Ideas Succeed? Most Ideas Do Not Persist ....
  5. 5. Motivation for ProjectWhy Do Certain Paradigms, Histories, Ideas Succeed? Most Ideas Do Not Persist .... Function of the ‘Quality’ of the Idea
  6. 6. Motivation for ProjectWhy Do Certain Paradigms, Histories, Ideas Succeed? Most Ideas Do Not Persist .... Function of the ‘Quality’ of the Idea Social Factors also Influence the Spread of Ideas
  7. 7. Positive Legal Theory
  8. 8. Positive Legal TheoryLaw Professors are Important Actors
  9. 9. Positive Legal TheoryLaw Professors are Important Actors Repositories / Distributors of information
  10. 10. Positive Legal TheoryLaw Professors are Important Actors Repositories / Distributors of information Agents of Socialization
  11. 11. Positive Legal TheoryLaw Professors are Important Actors Repositories / Distributors of information Agents of Socialization Socialize Future lawyers, Judges & law Professors
  12. 12. Positive Legal TheoryLaw Professors are Important Actors Repositories / Distributors of information Agents of Socialization Socialize Future lawyers, Judges & law ProfessorsResponsible for Developing Particular Legal Ideas (Brandwein (2007) ; Graber (1991), etc.)
  13. 13. Positive Legal TheoryLaw Professors are Important Actors Repositories / Distributors of information Agents of Socialization Socialize Future lawyers, Judges & law ProfessorsResponsible for Developing Particular Legal Ideas (Brandwein (2007) ; Graber (1991), etc.)Law Professor Behavior is a ImportantComponent of Positive Legal Theory
  14. 14. Social Network Analysis
  15. 15. Social Network AnalysisMethod for Tracking Social Connections, etc.
  16. 16. Social Network AnalysisMethod for Tracking Social Connections, etc.Method for Characterizing Diffusion / Info Flow
  17. 17. Social Network AnalysisMethod for Tracking Social Connections, etc.Method for Characterizing Diffusion / Info FlowMethod for Ranking Components basedupon Various Graph Based Measures
  18. 18. Basic Introduction toSocial Network Analysis
  19. 19. Terminology & ExamplesNODES
  20. 20. Terminology & ExamplesNODES Actors
  21. 21. Terminology & ExamplesNODES Institutions Actors
  22. 22. Terminology & ExamplesNODES Institutions Actors States/Countries
  23. 23. Terminology & ExamplesNODES Institutions Firms Actors States/Countries
  24. 24. Terminology & ExamplesNODES Institutions Firms Actors Other States/Countries
  25. 25. Terminology & Examples
  26. 26. Terminology & ExamplesExample: Nodes in an actor- based social Network
  27. 27. Terminology & Examples AliceExample: Nodes in an actor- based social Network
  28. 28. Terminology & Examples AliceExample: Nodes in an actor- based social Network Bill
  29. 29. Terminology & Examples AliceExample: Nodes in an actor- based social Network Bill Carrie
  30. 30. Terminology & Examples AliceExample: Nodes in an actor- based social Network Bill Carrie David
  31. 31. Terminology & Examples AliceExample: Nodes in an actor- based social Network Bill Carrie David Ellen
  32. 32. Terminology & Examples AliceExample: Nodes in an actor- based social Network Bill CarrieHow Can We Represent TheRelevant Social Relationships? David Ellen
  33. 33. Terminology & Examples AliceArcs Bill CarrieEdges David Ellen
  34. 34. Terminology & ExamplesArcs Alice Bill CarrieEdges David Ellen
  35. 35. Terminology & ExamplesArcs Carrie Alice BillEdges David Ellen
  36. 36. Terminology & Examples DavidCarrie Alice Bill A Full Representation of the Social Network Ellen
  37. 37. Terminology & Examples DavidCarrie Alice Bill A Full Representation Ellen of the Social Network (With Node Weighting)
  38. 38. Social Network Analysis of the American Law Professoriate
  39. 39. Cornell University Law School
  40. 40. Cornell University Law School
  41. 41. Cornell University Law School
  42. 42. Cornell University Law School
  43. 43. Cornell University Law School
  44. 44. Cornell University Law School
  45. 45. Building A Graph Theoretic RepresentationHarvard Penn Cornell
  46. 46. Building A Graph Theoretic RepresentationHarvard Penn Cornell
  47. 47. Building A Graph Theoretic RepresentationHarvard Penn Cornell
  48. 48. Building A Graph Theoretic RepresentationHarvard Penn Cornell
  49. 49. Building the Full Dataset
  50. 50. Building the Full Dataset
  51. 51. Building the Full Dataset
  52. 52. Building the Full Dataset
  53. 53. Building the Full Dataset ....
  54. 54. Full Data Set ....
  55. 55. Full Data Set7,054 Law Professors ! p = {p1, p2, ... p7240} ....
  56. 56. Full Data Set7,054 Law Professors ! p = {p1, p2, ... p7240}184 ABA Accredited Institutions n = {n1 , n2, … n184} ....
  57. 57. Visualizing a Full Network
  58. 58. Visualizing a Full Network
  59. 59. Visualizing a Full Network
  60. 60. Visualizing a Full Network
  61. 61. Visualizing a Full Network
  62. 62. Zoomable Visualization Available @http://computationallegalstudies.com/
  63. 63. Zoomable Visualization Available @http://computationallegalstudies.com/
  64. 64. A Graph-Based Measure of Centrality
  65. 65. Hub Score
  66. 66. Hub ScoreSimilar to the Google PageRank™ Algorithm Measure who is important? Measure who is important to who is important? Run Analysis Recursively...
  67. 67. Hub ScoreSimilar to the Google PageRank™ Algorithm Measure who is important? Measure who is important to who is important? Run Analysis Recursively...Score Each Institution’s Placements byNumber and Quality of Links Normalized Score (0, 1]
  68. 68. HubScore Rank 1 US News Peer Assessment 1 Hub Score 1.0000000 Institution Harvard Hub Scores 2 1 0.9048631 Yale 3 5 0.8511497 Michigan 4 4 0.7952253 Columbia 5 5 0.7737389 Chicago 6 8 0.7026757 NYU 7 1 0.6668868 Stanford Hub US News Hub 8 8 0.6607399 Berkeley Score Peer Institution Score Rank Assessment 9 10 0.6457157 Penn 10 10 0.6255498 Georgetown 26 24 0.1999686 UC Hastings 11 5 0.5854464 Virginia 27 34 0.1974877 Tulane 12 14 0.5014904 Northwestern 28 28 0.1749897 USC 13 10 0.4138745 Duke 29 35 0.1702638 Ohio State 14 10 0.4075353 Cornell 30 24 0.1586516 Boston College 15 15 0.3977734 Texas 31 72 0.1543831 Syracuse 16 28 0.3787268 Wisconsin 32 19 0.1537236 UNC 17 19 0.3273598 UCLA 33 56 0.1525355 Case Western 18 24 0.2959581 Illinois 34 82 0.1511569 Northeastern 19 28 0.2919847 Boston University 35 19 0.1428239 Notre Dame 20 28 0.2513371 Minnesota 36 56 0.1286375 Temple 21 24 0.2403289 Iowa 37 82 0.1232289 Rutgers Camden 22 28 0.2275534 Indiana 38 56 0.1227421 Kansas 23 19 0.2235015 George 39 64 0.1213358 Connecticut 24 16 0.2174677 Washington Vanderbilt 40 47 0.1198901 American 25 41 0.2012442 Florida 41 34 0.1162101 Fordham 42 64 0.1150860 Kentucky 43 106 0.1148082 Howard 44 47 0.1125957 Maryland 45 28 0.1101975 William & Mary 46 56 0.1058079 Colorado 47 19 0.1041129 Emory 48 17 0.1031490 Washington & Lee 49 72 0.1027442 Miami 50 103 0.1006172 SUNY Buffalo
  69. 69. Hub US News Peer Hub InstitutionScore Rank Assessment Score Score 26 24 0.1999686 UC Hastings 27 34 0.1974877 Tulane 28 28 0.1749897 USC 29 35 0.1702638 Ohio State 30 24 0.1586516 Boston College 31 72 0.1543831 Syracuse 32 19 0.1537236 UNC 33 56 0.1525355 Case Western 34 82 0.1511569 Northeastern 35 19 0.1428239 Notre Dame 36 56 0.1286375 Temple 37 82 0.1232289 Rutgers Camden 38 56 0.1227421 Kansas 39 64 0.1213358 Connecticut 40 47 0.1198901 American 41 34 0.1162101 Fordham 42 64 0.1150860 Kentucky 43 106 0.1148082 Howard 44 47 0.1125957 Maryland 45 28 0.1101975 William & Mary 46 56 0.1058079 Colorado 47 19 0.1041129 Emory 48 17 0.1031490 Washington & Lee 49 72 0.1027442 Miami 50 103 0.1006172 SUNY Buffalo
  70. 70. Hub US News Peer Hub InstitutionScore Rank Assessment Score Score 26 24 0.1999686 UC Hastings 27 34 0.1974877 Tulane 28 28 0.1749897 USC 29 35 0.1702638 Ohio State 30 24 0.1586516 Boston College 31 72 0.1543831 Syracuse 32 19 0.1537236 UNC 33 56 0.1525355 Case Western 34 82 0.1511569 Northeastern 35 19 0.1428239 Notre Dame 36 56 0.1286375 Temple 37 82 0.1232289 Rutgers Camden 38 56 0.1227421 Kansas 39 64 0.1213358 Connecticut 40 47 0.1198901 American 41 34 0.1162101 Fordham 42 64 0.1150860 Kentucky 43 106 0.1148082 Howard 44 47 0.1125957 Maryland 45 28 0.1101975 William & Mary 46 56 0.1058079 Colorado 47 19 0.1041129 Emory 48 17 0.1031490 Washington & Lee 49 72 0.1027442 Miami 50 103 0.1006172 SUNY Buffalo
  71. 71. Hub US News Peer Hub InstitutionScore Rank Assessment Score Score 26 24 0.1999686 UC Hastings 27 34 0.1974877 Tulane 28 28 0.1749897 USC 29 35 0.1702638 Ohio State 30 24 0.1586516 Boston College 31 72 0.1543831 Syracuse 32 19 0.1537236 UNC 33 56 0.1525355 Case Western 34 82 0.1511569 Northeastern 35 19 0.1428239 Notre Dame 36 56 0.1286375 Temple 37 82 0.1232289 Rutgers Camden 38 56 0.1227421 Kansas 39 64 0.1213358 Connecticut 40 47 0.1198901 American 41 34 0.1162101 Fordham 42 64 0.1150860 Kentucky 43 106 0.1148082 Howard 44 47 0.1125957 Maryland 45 28 0.1101975 William & Mary 46 56 0.1058079 Colorado 47 19 0.1041129 Emory 48 17 0.1031490 Washington & Lee 49 72 0.1027442 Miami 50 103 0.1006172 SUNY Buffalo
  72. 72. Hub US News Peer Hub InstitutionScore Rank Assessment Score Score 26 24 0.1999686 UC Hastings 27 34 0.1974877 Tulane 28 28 0.1749897 USC 29 35 0.1702638 Ohio State 30 24 0.1586516 Boston College 31 72 0.1543831 Syracuse 32 19 0.1537236 UNC 33 56 0.1525355 Case Western 34 82 0.1511569 Northeastern 35 19 0.1428239 Notre Dame 36 56 0.1286375 Temple 37 82 0.1232289 Rutgers Camden 38 56 0.1227421 Kansas 39 64 0.1213358 Connecticut 40 47 0.1198901 American 41 34 0.1162101 Fordham 42 64 0.1150860 Kentucky 43 106 0.1148082 Howard 44 47 0.1125957 Maryland 45 28 0.1101975 William & Mary 46 56 0.1058079 Colorado 47 19 0.1041129 Emory 48 17 0.1031490 Washington & Lee 49 72 0.1027442 Miami 50 103 0.1006172 SUNY Buffalo
  73. 73. Hub US News Peer Hub InstitutionScore Rank Assessment Score Score 26 24 0.1999686 UC Hastings 27 34 0.1974877 Tulane 28 28 0.1749897 USC 29 35 0.1702638 Ohio State 30 24 0.1586516 Boston College 31 72 0.1543831 Syracuse 32 19 0.1537236 UNC 33 56 0.1525355 Case Western 34 82 0.1511569 Northeastern 35 19 0.1428239 Notre Dame 36 56 0.1286375 Temple 37 82 0.1232289 Rutgers Camden 38 56 0.1227421 Kansas 39 64 0.1213358 Connecticut 40 47 0.1198901 American 41 34 0.1162101 Fordham 42 64 0.1150860 Kentucky 43 106 0.1148082 Howard 44 47 0.1125957 Maryland 45 28 0.1101975 William & Mary 46 56 0.1058079 Colorado 47 19 0.1041129 Emory 48 17 0.1031490 Washington & Lee 49 72 0.1027442 Miami 50 103 0.1006172 SUNY Buffalo
  74. 74. Hub US News Peer Hub InstitutionScore Rank Assessment Score Score 26 24 0.1999686 UC Hastings 27 34 0.1974877 Tulane 28 28 0.1749897 USC 29 35 0.1702638 Ohio State 30 24 0.1586516 Boston College 31 72 0.1543831 Syracuse 32 19 0.1537236 UNC 33 56 0.1525355 Case Western 34 82 0.1511569 Northeastern 35 19 0.1428239 Notre Dame 36 56 0.1286375 Temple 37 82 0.1232289 Rutgers Camden 38 56 0.1227421 Kansas 39 64 0.1213358 Connecticut 40 47 0.1198901 American 41 34 0.1162101 Fordham 42 64 0.1150860 Kentucky 43 106 0.1148082 Howard 44 47 0.1125957 Maryland 45 28 0.1101975 William & Mary 46 56 0.1058079 Colorado 47 19 0.1041129 Emory 48 17 0.1031490 Washington & Lee 49 72 0.1027442 Miami 50 103 0.1006172 SUNY Buffalo
  75. 75. Distribution ofSocial Authority
  76. 76. Top 20 Institutions (By Raw Placements)1,000 800 600400200 BU IllinoisMinnesota Northwesternexas T Duke UCLA Cornell isconsin W 0 NYU Stanford Berkeley UVA GeorgetownPenn Harvard Yale Michigan Columbia Chicago
  77. 77. ! !
  78. 78. Highly Skewed Nature of Legal Systems!
  79. 79. Highly Skewed Nature of Legal Systems!Katz & Stafford 2010
  80. 80. Highly Skewed Nature of Legal Systems!Katz & Stafford 2010
  81. 81. Highly Skewed Nature of Legal Systems Smith 2007!Katz & Stafford 2010
  82. 82. Highly Skewed Nature of Legal Systems Smith 2007!Katz & Stafford 2010
  83. 83. Highly Skewed Nature of Legal Systems Smith 2007!Katz & Stafford 2010 Post & Eisen 2000
  84. 84. Implications for Rankings
  85. 85. Implications for Rankings Rankings only Imply Ordering ( >, =, < )
  86. 86. Implications for Rankings Rankings only Imply Ordering ( >, =, < ) End Users tend to Conflate Ranks with Linearized Distances Between Units (Tversky 1977)
  87. 87. Implications for Rankings Rankings only Imply Ordering ( >, =, < ) End Users tend to Conflate Ranks with Linearized Distances Between Units (Tversky 1977) Non-Stationary Distances Between Entities Both Trivial and Large Distances Linearity Heuristic Often Works Assuming Linearity Can Prove Misleading
  88. 88. Computational Model of Information Diffusion
  89. 89. Why Computational Simulation?
  90. 90. Why Computational Simulation?History only Provides a Single Model Run
  91. 91. Why Computational Simulation?History only Provides a Single Model RunComputational Simulation allows ... Consideration of Alternative “States of the world” Evaluation of Counterfactuals
  92. 92. Computational Model of Information Diffusion
  93. 93. Computational Model of Information DiffusionWe Apply a simple Disease Model to Consider the Spread of Ideas, etc.
  94. 94. Computational Model of Information DiffusionWe Apply a simple Disease Model to Consider the Spread of Ideas, etc.Clear Tradeoff Between Structural Position in the Network and “Idea Infectiousness”
  95. 95. A Basic Description of the Model
  96. 96. A Basic Description of the ModelConsider a Hypothetical Idea Releasedat a Given Institution
  97. 97. A Basic Description of the ModelConsider a Hypothetical Idea Releasedat a Given InstitutionInfectiousness Probability = p
  98. 98. A Basic Description of the ModelConsider a Hypothetical Idea Releasedat a Given InstitutionInfectiousness Probability = pInfect neighbors, neighbors-neighbors, etc.
  99. 99. A Basic Description of the ModelConsider a Hypothetical Idea Releasedat a Given InstitutionInfectiousness Probability = pInfect neighbors, neighbors-neighbors, etc.Two Forms Diffusion... Direct Socialization Signal Giving to Former Students
  100. 100. Channels of Diffusion
  101. 101. Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics
  102. 102. Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics Legal Socialization / Training
  103. 103. Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics Legal Socialization / Training Judicial Decisions, Law Reviews, Other Materials
  104. 104. Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics Legal Socialization / Training Judicial Decisions, Law Reviews, Other Materials Academic Conferences, Other Professional Orgs
  105. 105. Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics Legal Socialization / Training Judicial Decisions, Law Reviews, Other Materials Academic Conferences, Other Professional Orgs SSRN, Legal Blogosphere, etc.
  106. 106. Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics Legal Socialization / Training Judicial Decisions, Law Reviews, Other Materials Academic Conferences, Other Professional Orgs SSRN, Legal Blogosphere, etc. Other Channels of Information Dissemination
  107. 107. A Sample Run of the Model
  108. 108. A Sample Run of the Model
  109. 109. A Sample Run of the Model
  110. 110. A Sample Run of the Model
  111. 111. A Sample Run of the Model
  112. 112. A Sample Run of the Model
  113. 113. A Sample Run of the Model
  114. 114. A Sample Run of the Model
  115. 115. A Sample Run of the Model
  116. 116. A Sample Run of the Model
  117. 117. A Sample Run of the Model
  118. 118. A Sample Run of the Model
  119. 119. A Sample Run of the Model
  120. 120. A Sample Run of the Model
  121. 121. A Sample Run of the Model
  122. 122. Run a Simulationon Your Desktop
  123. 123. Run a Simulationon Your Desktop
  124. 124. Run a Simulationon Your Desktop
  125. 125. Run a Simulationon Your Desktop (Requires Java 5.0 or Higher)
  126. 126. Run a Simulationon Your Desktop (Requires Java 5.0 or Higher)
  127. 127. Run a Simulationon Your Desktop (Requires Java 5.0 or Higher)
  128. 128. Run a Simulation on Your Desktop (Requires Java 5.0 or Higher)http://computationallegalstudies.com/2009/04/22/the-revolution-will-not-be-televised-but-will-it- come-from-harvard-or-yale-a-network-analysis-of-the-american-law-professoriate-part-iii/
  129. 129. From a Single Run toConsensus Diffusion Plot
  130. 130. From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration
  131. 131. From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration
  132. 132. From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration
  133. 133. From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/
  134. 134. From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/Regular Programming Language TypicallyRequired for Full Scale Implementation
  135. 135. From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/Regular Programming Language TypicallyRequired for Full Scale ImplementationWe Used Python
  136. 136. From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/Regular Programming Language TypicallyRequired for Full Scale ImplementationWe Used Python
  137. 137. From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/Regular Programming Language TypicallyRequired for Full Scale ImplementationWe Used Python http://www.python.org/
  138. 138. From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/Regular Programming Language TypicallyRequired for Full Scale ImplementationWe Used Python http://www.python.org/ Object Oriented Programming Language
  139. 139. From a Single Run toConsensus Diffusion Plot
  140. 140. From a Single Run to Consensus Diffusion PlotRepeated the Diffusion Simulation
  141. 141. From a Single Run to Consensus Diffusion PlotRepeated the Diffusion SimulationHundreds of Model Runs Per School
  142. 142. From a Single Run to Consensus Diffusion PlotRepeated the Diffusion SimulationHundreds of Model Runs Per SchoolYielded a Consensus Plot for Each School
  143. 143. From a Single Run to Consensus Diffusion PlotRepeated the Diffusion SimulationHundreds of Model Runs Per SchoolYielded a Consensus Plot for Each SchoolResults for Five Emblematic Schools Exponential, linear and sub-linear
  144. 144. Computational Simulation of Diffusion uponthe Structure of the American Legal Academy !
  145. 145. Some PotentialModel Improvements?
  146. 146. Some Potential Model Improvements?Differential Host Susceptibility
  147. 147. Some Potential Model Improvements?Differential Host SusceptibilityCountervailing Information / Paradigms
  148. 148. Some Potential Model Improvements?Differential Host SusceptibilityCountervailing Information / ParadigmsS I R Model Susceptible-Infected-Recovered
  149. 149. Directions forFuture Research
  150. 150. Directions for Future ResearchLongitudinal Data Hiring/Placement/Laterals Current Collecting Data
  151. 151. Directions for Future ResearchLongitudinal Data Hiring/Placement/Laterals Current Collecting DataDatabase Linkage to Articles/Citations Working with Content Providers
  152. 152. Directions for Future ResearchLongitudinal Data Hiring/Placement/Laterals Current Collecting DataDatabase Linkage to Articles/Citations Working with Content ProvidersEmpirical Evaluation of Simulation Computational Lingusitics Text Mining, Sentiment Coding
  153. 153. Thanks for Support

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