魏希德 － 賴頓大學

Hilde De Weerdt, Leiden University

MARKUS

Huizhu lu. Informant network by citation frequency. Frequencies rise from 1 at the periphery to 2, 3, 4, 5, 7, 8, 11, 15, and 24 closer to the core. 308 is the total number of informants excluding Wang Mingqing and 553 is the total number of citations excluding self-citations.

Huizhu lu. Distribution of court vs. local and regional office holding for authors and interlocutors (in column 2 only those with 3 or more offices are counted in order to exclude those who obtained honorary titles only)

court local and regionaltotal (out of 201 with office)

total (per type)

authors85 76.5%

18 16.2%

111 55.22%

111/168 66%

interlocutors30 57.7%

18 34.6%

52 25.8%

52/117 44.44%

Huizhu lu. Distribution of informant network in Huizhu lu by residential (1) and office (2) address. Each hexagon represents a county or prefectural seat where at least one informant was resident.

Background: Why Digital Methods?

✤ What?

➡ the use of computer technology in research on humanities subjects and the critical self-reflection on how digital methods shape research

✤ Why?

➡ allows for better philology

➡ facilitates systematic and comprehensive reading in addition to anecdotal interpretation

➡ allows for multi-perspectival exploration and analysis

➡ raises awareness of the mediological effects of computational designs

➡ enables evaluation and re-use of primary research materials and data

✤ How?

➡ online reading and analysis of notebooks and letters (best and underutilized sources for personal and collective reception and response to political and cultural events)

➡ bibliographic database of notebooks in print

➡ set of notebooks and letters

➡ collaboration and integration with related projects

➡ China Biographical Database (Harvard University, Beijing University, Academia Sinica)

➡ China Historical GIS (Harvard University, Fudan University)

➡ Automated tagging and text mining (National Taiwan University---Harvard)

MARKUS: http://dh.chinese-empires.eu/beta/

http://dh.chinese-empires.eu/beta/

CLOSE READING

http://chinese-empires.eu/analysis/tools/cbdb-mac-dictionary/

How to stay in touch?

✤ Communication and Empire : Chinese Empires in Comparative Perspective http://chinese-empires.eu/

✤ twitter @comparativehist

✤ DID-ACTE: Digging into Data: Automating Chinese Text Extraction http://did-acte.org/

✤ twitter @did_acte

Major moments in the history of factionalism

✤ late Han (2nd C): the Great Proscription; ca 200 names

✤ late Tang (9th C): Niu and Li

✤ late Northern Song (1068-1104): Yuanyou (1086-94) -- blacklists (1102-1104); 309 names

EXPERIMENT 1 Centrality: co-occurrence frequencies

✤ Which names does a name co-occur with? How are names clustered together? -> How does the author interpret the social and political world around him?

✤ Huizhu 1 1166

✤ Huizhu 2 1193/4

name index year degree weighted betweenness)? 1135 339 423 0.099518559)� 1166 313 404 0.068396317��� 1194 300 380 0.090575875�� 1095 162 206 0.031062174)5 1160 181 206 0.024402052( 1126 161 205 0.022044045)= 1085 163 204 0.028259857�4 1095 144 167 0.026670356�@ 1146 137 164 0.021269205#: 1149 139 164 0.032177688��" 1149 122 138 0.003799733�& 1145 118 135 0.003277197�4 1095 115 133 0.020737006,1% 1130 115 130 0.005033286��� 1142 110 122 0.001588977)0; 976 107 119 0.011428429)6 1100 97 114 0.011278717�� 1068 96 110 0.011555285

Centrality: (Huizhu 1, 1166) overlap and dependence

✤ To what extent do two names overlap?

✤ A high degree of exclusive association with and a higher number of exclusive associations of a name suggest higher centrality

overlap name 1 name 2 total 1 total 2 dep 1 dep 2

3 '> �� 6 3 0.5 1

3 )� )6 7 30.428571 1

5 )3 ) 10 6 0.50.833333

5 )= )- 9 70.5555560.714286

4 ,�� ,+� 6 60.6666670.666667

4 )- ) 7 60.5714290.666667

4 )� ) 7 60.5714290.666667

4 )= ) 9 60.4444440.666667

6 ) ). 6 10 1 0.6

4 )- )0; 7 70.5714290.571429

4 )? )0; 8 7 0.50.571429

4 ). )0; 10 7 0.40.571429

5 )- ). 7 100.714286 0.5

5 )- )3 7 100.714286 0.5

3 ,+� �7 6 6 0.5 0.5

5 )3 ). 10 10 0.5 0.5

Centrality IV: (Huizhu 2, 1193/4) overlap and dependence

overlap name 1 name 2 total 1 total 2 dep 1 dep 2

4 )? �B 38 50.10526316 0.8

5 )� �& 31 70.161290320.71428571

5 )� ��" 31 70.161290320.71428571

4 )? �"/ 38 60.105263160.66666667

3 �B (8 5 5 0.6 0.6

3 $�� (8 7 50.42857143 0.6

3 �* (8 10 5 0.3 0.6

3 �4 �� 20 5 0.15 0.6

3 )� �!2 31 50.09677419 0.6

3 )? (8 38 50.07894737 0.6

4 )? $�� 38 70.105263160.57142857

5 ��� �� 14 90.357142860.55555556

3 �7 �< 5 6 0.6 0.5

3 $�� �9 7 60.42857143 0.5

4 )3 ) 12 80.33333333 0.5

5 )� ,1% 31 100.16129032 0.5

3 )= �"/ 21 60.14285714 0.5

3 )= )- 21 60.14285714 0.5

3 #: �9 29 60.10344828 0.5

3 )� �@ 31 60.09677419 0.5

overlap name 1 name 2 total 1 total 2 dep 1 dep 2

4 �7 ��� 5 14 0.80.28571429

4 (8 ( 5 23 0.80.17391304

4 �B #: 5 29 0.80.13793103

5 $�� ( 7 230.714285710.2173913

4 �9 ( 6 230.666666670.17391304

4 �9 ��� 6 140.666666670.28571429

4 �"/ ( 6 230.666666670.17391304

4 �"/ �� 6 220.666666670.18181818

3 �7 �< 5 6 0.6 0.5

3 �7 �� 5 9 0.60.33333333

3 (8 #: 5 29 0.60.10344828

3 �!2 #: 5 29 0.60.10344828

6 (A ( 10 23 0.60.26086957

3 �B ( 5 23 0.60.13043478

3 �B (8 5 5 0.6 0.6

3 �B $�� 5 7 0.60.42857143

3 �B �* 5 10 0.6 0.3

4 �& ,1% 7 100.57142857 0.4

4 $�� ��� 7 140.571428570.28571429

4 $�� #: 7 290.571428570.13793103

4 ��" ,1% 7 100.57142857 0.4

3 �< �� 6 11 0.50.27272727

3 �< �� 6 9 0.50.33333333

3 �< ��� 6 14 0.50.21428571

3 �"/ ��� 6 14 0.50.21428571

5 (A �� 10 22 0.50.22727273

Top receivers of exclusive associations

name exclusive assoc

( 6

)� 6

��� 6

#: 5

EXPERIMENT 2 Name clustering

✤ When and how do names on 1102-1104 faction list begin to cluster?

✤ Sources:

✦ letters written by those on faction lists

✦ all Quan Song wen documents

✤ Problems: alternate names, official titles

�',)���� ���$�++�)*�*�&+��0� ��(�)*'&*1����� ')�."#�"�)��#(#�&+*�"�-�����&�#��&+# #�������� )'%�#&��/��� ��+")',!"�������������&+#'&*������$�++�)*�+'�(�)*'&*�'&�+"��$#*+��#&-'$-#&!���(�)*'&*

�',)�����*����,+"')*� ����������������,+"')*����������'�,%�&+*�#&� ����������"�)��+�)*���&+#'&*��*��&�' ����&�%�*���������%�&+#'&*�' ����(�)*'&*�#&��� ���'�,%�&+*�.)#++�&��0������,+"')*

�',)�����*����,+"')*��������������������,+"')*����������'�,%�&+*�#&���� ������"�)��+�)*���&+#'&*��*��&�' ����&�%�*��������%�&+#'&*�' ����(�)*'&*�#&������'�,%�&+*�.)#++�&��0������,+"')*

�',)�����*����,+"')*� ����������������,+"')*�� �� ��'�,%�&+*�#&�����������"�)��+�)*���&+#'&*��*��&�' ���&�%�*�������%�&+#'&*�' � �(�)*'&*�#&����� ��'�,%�&+*�.)#++�&��0� � ��,+"')*