In [0]:
!ls sample_data/
anscombe.json		      mnist_test.csv
california_housing_test.csv   mnist_train_small.csv
california_housing_train.csv  README.md
In [5]:
!pip uninstall knowknow-amcgail
Uninstalling knowknow-amcgail-0.1.1:
  Would remove:
    /usr/local/lib/python3.6/dist-packages/knowknow/*
    /usr/local/lib/python3.6/dist-packages/knowknow_amcgail-0.1.1.dist-info/*
Proceed (y/n)? y
  Successfully uninstalled knowknow-amcgail-0.1.1
In [8]:
!pip install -U knowknow-amcgail==0.1.2
import sys; sys.path.append(_dh[0].split("knowknow")[0])
from knowknow import *
ERROR: Could not find a version that satisfies the requirement knowknow-amcgail==0.1.2 (from versions: 0.0.1, 0.1.0, 0.1.1)
ERROR: No matching distribution found for knowknow-amcgail==0.1.2
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-8-0b0d2df9e331> in <module>()
      1 get_ipython().system('pip install -U knowknow-amcgail==0.1.2')
      2 import sys; sys.path.append(_dh[0].split("knowknow")[0])
----> 3 from knowknow import *

/usr/local/lib/python3.6/dist-packages/knowknow/__init__.py in <module>()
     18 import yaml
     19 DOCS = yaml.load(
---> 20     Path(BASEDIR,"documentation.yaml").open('r',encoding='utf8'),
     21     Loader=yaml.FullLoader
     22 )

/usr/lib/python3.6/pathlib.py in open(self, mode, buffering, encoding, errors, newline)
   1181             self._raise_closed()
   1182         return io.open(str(self), mode, buffering, encoding, errors, newline,
-> 1183                        opener=self._opener)
   1184 
   1185     def read_bytes(self):

/usr/lib/python3.6/pathlib.py in _opener(self, name, flags, mode)
   1035     def _opener(self, name, flags, mode=0o666):
   1036         # A stub for the opener argument to built-in open()
-> 1037         return self._accessor.open(self, flags, mode)
   1038 
   1039     def _raw_open(self, flags, mode=0o777):

/usr/lib/python3.6/pathlib.py in wrapped(pathobj, *args)
    385         @functools.wraps(strfunc)
    386         def wrapped(pathobj, *args):
--> 387             return strfunc(str(pathobj), *args)
    388         return staticmethod(wrapped)
    389 

FileNotFoundError: [Errno 2] No such file or directory: '/usr/local/lib/python3.6/dist-packages/knowknow/documentation.yaml'
In [0]:
dtype = 't'
database_name = "sociology-jstor-basicall"
In [0]:
ify = comb(dtype,'fy')
In [0]:
cnt = get_cnt('%s.doc'%database_name, ['fy',ify,dtype])
ysum = load_variable('%s.%s.ysum' % (database_name, dtype))
Loaded keys: dict_keys(['fy', 'fy.t', 't'])
Available keys: ['c', 'c.c', 'c.c.fy', 'c.fa', 'c.fj', 'c.fy', 'c.t', 'fa', 'fa.fj.fy', 'fj', 'fj.fy', 'fj.t', 'fy', 'fy.t', 't', 't.t']
In [0]:
list(ysum)[:10]
Out[0]:
['relationship',
 'multiple',
 'protestant',
 'religious',
 'thesis',
 'object',
 'capitalism',
 'weber',
 'takes',
 'marx']

Make the publication year the base year

In [0]:
Counter(dict(cnt[dtype])).most_common(10)
Out[0]:
[(t(t='social'), 25329),
 (t(t='one'), 20959),
 (t(t='also'), 20860),
 (t(t='research'), 19916),
 (t(t='studies'), 19146),
 (t(t='may'), 18278),
 (t(t='many'), 14907),
 (t(t='among'), 14857),
 (t(t='work'), 14856),
 (t(t='however'), 14688)]
In [0]:
list(ysum)[:5]
Out[0]:
['relationship', 'multiple', 'protestant', 'religious', 'thesis']
In [0]:
all_years = np.array([[1,2],[3,4]])
In [0]:
 
Out[0]:
array([[0.25      , 0.33333333],
       [0.75      , 0.66666667]])
In [0]:
def badass_heatmap(whats, fnargs=[], RANGE=None, 
                   markers = {}, markersize=50, align='left',
                   proportional = False, MAXYEAR=2018,
                   **kwargs):
    whats = list(whats)

    all_years = []

    if RANGE is None:
        RANGE = 2015 - min( x.fy for x in cnt[ify] if (getattr(x,dtype),) in whats )
        
    max_cnt = max( [ c for x,c in cnt[ify].items() if c > 0 and (getattr(x,dtype),) in whats ] )
    
    start_years = []
    
    if align == 'left':
        for what in whats:
            what = what[0] #get the item out of the tuple

            if what not in ysum:
                continue

            start_year = min( [ x.fy for x in cnt[ify] if cnt[ify][x] > 0 and getattr(x,dtype)==what ] )

            def get_val(y):

                nanval = -max_cnt/5
                #if what in markers and y in markers[what]:
                #    return nanval

                if y < start_year or y > MAXYEAR:
                    return nanval

                myiy = make_cross({"fy":y, dtype:what})
                return cnt[ify][myiy]

            year_l = [ get_val(y) for y in range(start_year, start_year+RANGE)]
            all_years.append(year_l)
            start_years.append(start_year)
            
    elif align == 'right':
        for what in whats:
            what = what[0] #get the item out of the tuple

            if what not in ysum:
                continue

            start_year = MAXYEAR - RANGE

            def get_val(y):

                nanval = -max_cnt/5
                #if what in markers and y in markers[what]:
                #    return nanval

                if y < start_year or y > MAXYEAR:
                    return nanval

                myiy = make_cross({"fy":y, dtype:what})
                return cnt[ify][myiy]

            year_l = [ get_val(y) for y in range(start_year, start_year+RANGE)]
            all_years.append(year_l)
            start_years.append(start_year)

    all_years = np.array(all_years)
    
    if proportional is not None:
        if proportional == 'columns':
            all_years = all_years/all_years.sum(axis=0)[None,:]
        if proportional == 'rows':
            all_years = all_years/all_years.sum(axis=1)[:,None]

    #fig, ax = plt.subplots(figsize=(30,10))
    #sns.heatmap(all_years, ax=ax)

    # sorts by their closest neighbors

    from scipy.spatial.distance import pdist, squareform
    distances = np.array([
        [
            np.sum( np.abs(year1[i]-year2[i]) if (year1[i] != -10 and year2[i] != -10) else -10 for i in range(year1.shape[0]) )
            for year2 in all_years
        ]
        for year1 in all_years
    ])

    seq = [0]
    while len(seq) < all_years.shape[0]:
        last_one = seq[-1]
        which_done = np.array([ samp in seq for samp in range( all_years.shape[0] )])

        minv = None
        mini = None
        for i in range(distances.shape[0]):
            if i in seq:
                continue

            v = distances[i,last_one]
            if minv is None or v < minv:
                mini = i
                minv = v

        seq.append(mini)

    fig, ax = plt.subplots(figsize=(30,10))
    sns.heatmap(all_years[seq,], ax=ax, **kwargs)
    
    mx = []
    my = []
    mstyle = []
    
    for wi, (what,years) in enumerate(markers.items()):
        which_what = whats.index((what,))
        my_start = start_years[which_what]
        which_row = seq.index( which_what )
        
        for year in years:
            mx.append( year-my_start+0.5 )
            my.append( which_row+0.5 )
            mstyle.append( years[year] ) #style!
    
    #print(markers, mx, my)
    if len(mx):
        for x,y,style in zip(mx,my,mstyle):
            ax.scatter([x], [y], color='black', s=markersize, marker=style)
    
    if align=='right':
        plt.xticks(
            [x+0.5 for x in range(0,RANGE,1)],
            range(MAXYEAR-RANGE,MAXYEAR,1)
        )
    save_figure("Top 100 lifespans (%s)" % ", ".join([database_name, dtype]+fnargs))
    plt.show()
    
    print(", ".join( "%d. %s" % (i, whats[seq[i]][0]) for i in range(len(whats)) ))
In [0]:
whats[:5]
Out[0]:
[('lack',), ('suggest',), ('concept',), ('could',), ('strong',)]
In [0]:
whats = [(x,) for x in ysum if (50 < ysum[x]['total'] < 1000)]
badass_heatmap(whats, ['random','raw'], proportional='rows', align='right', RANGE=40, MAXYEAR=2000)
c:\users\amcga\envs\citation-deaths\lib\site-packages\ipykernel_launcher.py:83: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.
0. relationship, 1. found, 2. research, 3. recent, 4. however, 5. although, 6. also, 7. example, 8. others, 9. used, 10. first, 11. work, 12. even, 13. well, 14. whether, 15. different, 16. way, 17. thus, 18. many, 19. much, 20. could, 21. often, 22. less, 23. important, 24. use, 25. level, 26. rather, 27. within, 28. high, 29. among, 30. life, 31. years, 32. results, 33. significant, 34. previous, 35. relative, 36. literature, 37. control, 38. given, 39. role, 40. based, 41. united, 42. states-united, 43. states, 44. national, 45. changes, 46. period, 47. support, 48. result, 49. likely, 50. especially, 51. particularly, 52. early, 53. women, 54. focused, 55. increased, 56. levels, 57. including, 58. despite, 59. negative, 60. suggests, 61. associated, 62. provide, 63. addition, 64. increase, 65. issues, 66. making, 67. form, 68. later, 69. set, 70. ones, 71. good, 72. limited, 73. theories, 74. increasing, 75. according, 76. strong, 77. moreover, 78. men, 79. higher, 80. tend, 81. three, 82. point, 83. another, 84. one, 85. two, 86. studies, 87. evidence, 88. since, 89. possible, 90. noted, 91. status, 92. findings, 93. structure, 94. social, 95. problem, 96. analysis, 97. several, 98. related, 99. various, 100. must, 101. though, 102. change, 103. report, 104. formal, 105. large, 106. little, 107. theoretical, 108. second, 109. effects, 110. rates, 111. effect, 112. model, 113. indeed, 114. long, 115. children, 116. parents, 117. families, 118. physical, 119. using, 120. perspective, 121. like, 122. individuals, 123. need, 124. course, 125. experience, 126. care, 127. action, 128. place, 129. become, 130. highly, 131. considered, 132. either, 133. personal, 134. empirical, 135. developed, 136. importance, 137. sense, 138. political, 139. members, 140. knowledge, 141. individual, 142. process, 143. attention, 144. past, 145. interests, 146. policy, 147. central, 148. forms, 149. around, 150. ways, 151. cultural, 152. practices, 153. resources, 154. potential, 155. contrast, 156. better, 157. models, 158. eg, 159. analyses, 160. researchers, 161. argued, 162. perceived, 163. child, 164. suggest, 165. context, 166. countries, 167. practice, 168. communities, 169. term, 170. following, 171. hand, 172. population, 173. made, 174. extent, 175. characteristics, 176. family, 177. generally, 178. percent, 179. nature, 180. therefore, 181. systems, 182. means, 183. basis, 184. measure, 185. measures, 186. age, 187. available, 188. sample, 189. independent, 190. variable, 191. areas, 192. recently, 193. working, 194. view, 195. organizational, 196. values, 197. position, 198. sociologists, 199. subject, 200. order, 201. interaction, 202. earlier, 203. degree, 204. concept, 205. patterns, 206. defined, 207. relationships, 208. religious, 209. behavior, 210. scale, 211. variables, 212. factor, 213. marriage, 214. least, 215. lack, 216. positive, 217. consistent, 218. include, 219. due, 220. lower, 221. without, 222. account, 223. compared, 224. single, 225. average, 226. participation, 227. psychological, 228. school, 229. survey, 230. information, 231. specific, 232. range, 233. third, 234. cases, 235. show, 236. ability, 237. established, 238. multiple, 239. future, 240. collective, 241. understanding, 242. argue, 243. capital, 244. market, 245. across, 246. scholars, 247. typically, 248. quality, 249. historical, 250. response, 251. low, 252. association, 253. identity, 254. gender
In [0]:
whats = Counter( dict( cnt[dtype].items() ) ).most_common(150)[50:100]
whats = [x[0] for x in whats]
badass_heatmap(whats, ['most_cits','raw'], align='right')
c:\users\amcga\envs\citation-deaths\lib\site-packages\ipykernel_launcher.py:76: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-15-0cbb3a735789> in <module>
      1 whats = Counter( dict( cnt[dtype].items() ) ).most_common(150)[50:100]
      2 whats = [x[0] for x in whats]
----> 3 badass_heatmap(whats, ['most_cits','raw'], align='right')

<ipython-input-12-c364a2ad7494> in badass_heatmap(whats, fnargs, RANGE, markers, markersize, align, **kwargs)
    121     plt.show()
    122 
--> 123     print(", ".join( "%d. %s" % (i, whats[seq[i]][0]) for i in range(len(whats)) ))

<ipython-input-12-c364a2ad7494> in <genexpr>(.0)
    121     plt.show()
    122 
--> 123     print(", ".join( "%d. %s" % (i, whats[seq[i]][0]) for i in range(len(whats)) ))

IndexError: list index out of range
In [0]:
whats[:10]
Out[0]:
[ta(ta='smith'),
 ta(ta='bourdieu'),
 ta(ta='cohen'),
 ta(ta='becker'),
 ta(ta='van'),
 ta(ta='wilson'),
 ta(ta='anderson'),
 ta(ta='davis'),
 ta(ta='williams'),
 ta(ta='weber')]
In [0]:
list(ysum)[:5]
Out[0]:
['bell', 'brechin', 'butler', 'cloke', 'frouws']
In [0]:
ysum['bell']
Out[0]:
{'maxprop': 0.07736389684813753,
 'maxcount': 176,
 'last': 2020,
 'death_8': None,
 'death_2': None,
 'death_6': None,
 'name': 'bell',
 'first': 1936,
 'death_3': None,
 'death_4': None,
 'death_7': None,
 'maxpropy': 1971,
 'death_max': None,
 'maxcounty': 2019,
 'death_5': None,
 'total': 3669,
 'death_9': None,
 'death_1': None,
 'totalprop': 3.081894642552492,
 'death_last': None}
In [0]:
# aim: to sort by something else interesting.
# I chose date of publication!!

for decade in range(1950,2020,10):

    names = list(cnt[ify].keys())
    names = [getattr(x,dtype) for x in names]
    names = [x for x in names if x in ysum]


    whats = sorted(cnt[ify], key=lambda x:-ysum[getattr(x,dtype)]['total'] if getattr(x,dtype) in ysum else 0 )
    whats = [x.ta for x in whats]
    whats = [x for x in whats if (x in ysum) and (decade <= ysum[x]['maxcounty'] < decade+10)]
    print(len(whats), "total")


    whatskeep = set()
    i=0
    while len(whatskeep) < 100 and i<len(whats):
        whatskeep.add( make_cross(ta=whats[i]) )
        i += 1
    whatskeep = list(whatskeep)
        
    cmap = sns.color_palette("cubehelix", 50)
    badass_heatmap(
        whatskeep, 
        ['top_cit_%ss'%decade,'raw'], 
        RANGE=None, 
        markers={x.ta:{decade+10:"<"} for x in whatskeep}, 
        markersize=30,
        cmap=cmap
    )
    
    plt.show()
2691 total
c:\users\amcga\envs\citation-deaths\lib\site-packages\ipykernel_launcher.py:50: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.
0. scodel, 1. overholser, 2. foenander, 3. gratiaen, 4. windells, 5. cassady, 6. kains, 7. kutak, 8. vonrhode, 9. wehrwein, 10. pelzel, 11. colcord, 12. liepmann, 13. schnepp, 14. dymond, 15. selekman, 16. ducoff, 17. hornseth, 18. orlansky, 19. benoitsmullyan, 20. bienstock, 21. waibel, 22. duncker, 23. gamio, 24. wecter, 25. powelson, 26. gloster, 27. blaustein, 28. bascom, 29. mangus, 30. mcclenahan, 31. fortune, 32. whetten, 33. ohlin, 34. mowrer, 35. terman, 36. roethlisberger
7388 total
0. hajda, 1. cowhig, 2. fellin, 3. fanfani, 4. pepitone, 5. newfield, 6. welford, 7. wahlke, 8. videbeck, 9. fenchel, 10. hoos, 11. glanzer, 12. silvert, 13. wearmouth, 14. wessen, 15. larrabee, 16. rostovtzeff, 17. spectorsky, 18. mccleery, 19. winterbottom, 20. omari, 21. nottingham, 22. sprott, 23. viteles, 24. greenblum, 25. massarik, 26. hetzler, 27. montague, 28. marvick, 29. frenkelbrunswik, 30. hogbin, 31. honigmann, 32. kuder, 33. anshen, 34. benney, 35. simey, 36. truxal, 37. majumdar, 38. dai, 39. haer, 40. lionberger, 41. marcson, 42. congalton, 43. busia, 44. greenblatt, 45. copp, 46. fliegel, 47. miyamoto, 48. jaco, 49. hoselitz, 50. klapper, 51. ausubel, 52. tappan, 53. shevky, 54. manis, 55. zborowski, 56. koos, 57. opler, 58. panunzio, 59. saenger, 60. coutu, 61. goldhamer, 62. vonwiese, 63. northrop, 64. radcliffebrown, 65. dunham, 66. brunner, 67. thurstone, 68. spykman, 69. burling, 70. mcelrath, 71. bredemeier, 72. coolidge, 73. gutkind, 74. altus, 75. laskin, 76. shaycoft, 77. broadus, 78. pfeil, 79. gebhard, 80. bastide, 81. hoebel, 82. erasmus, 83. dinitz, 84. konig, 85. mccord, 86. presthus, 87. burchinal, 88. marris, 89. menzel, 90. belknap, 91. hovland, 92. reissman, 93. cumming, 94. cloward, 95. clinard, 96. kluckhohn, 97. redfield, 98. loomis, 99. hollingshead
30594 total
0. blalock, 1. kuhn, 2. coser, 3. gibbs, 4. wilensky, 5. glock, 6. dahrendorf, 7. blauner, 8. mechanic, 9. heer, 10. skolnick, 11. demerath, 12. easton, 13. banfield, 14. runciman, 15. luckmann, 16. matza, 17. alford, 18. rex, 19. sudnow, 20. laslett, 21. liebow, 22. chambliss, 23. scanzoni, 24. heise, 25. parkin, 26. cicourel, 27. hodge, 28. moskos, 29. olsen, 30. blood, 31. rainwater, 32. litwak, 33. srole, 34. kahl, 35. bott, 36. herberg, 37. horton, 38. ehrlich, 39. cutright, 40. lukacs, 41. cartwright, 42. mcclelland, 43. galbraith, 44. seeman, 45. sussman, 46. shibutani, 47. bogue, 48. riesman, 49. bales, 50. greer, 51. middleton, 52. fromm, 53. winch, 54. komarovsky, 55. gerth, 56. tannenbaum, 57. newcomb, 58. lynd, 59. landis, 60. ogburn, 61. kornhauser, 62. stouffer, 63. sorokin, 64. mannheim, 65. hawley, 66. lemert, 67. myrdal, 68. bendix, 69. selznick, 70. yinger, 71. caplow, 72. inkeles, 73. rokeach, 74. nye, 75. nisbet, 76. siegel, 77. janowitz, 78. gusfield, 79. glazer, 80. ryder, 81. scheff, 82. moynihan, 83. treiman, 84. jencks, 85. stinchcombe, 86. greeley, 87. smelser, 88. etzioni, 89. eisenstadt, 90. shils, 91. hyman, 92. reiss, 93. lenski, 94. gouldner, 95. goode, 96. homans, 97. berelson, 98. lipset, 99. parsons
39368 total
0. joreskog, 1. offe, 2. bishop, 3. verbrugge, 4. bluestone, 5. bradburn, 6. bainbridge, 7. aronowitz, 8. kohlberg, 9. frisbie, 10. hibbs, 11. hindess, 12. unger, 13. hymes, 14. gorsuch, 15. oneill, 16. coombs, 17. modell, 18. lopata, 19. fienberg, 20. quinney, 21. spanier, 22. kerlinger, 23. miliband, 24. rodman, 25. hope, 26. paige, 27. boserup, 28. zurcher, 29. illich, 30. burch, 31. liska, 32. wrong, 33. lasch, 34. tittle, 35. doeringer, 36. hindelang, 37. spilerman, 38. zeitlin, 39. hadden, 40. schur, 41. blumberg, 42. chomsky, 43. breton, 44. spitzer, 45. antonovsky, 46. dye, 47. masters, 48. palmore, 49. hagen, 50. vandenberghe, 51. fuguitt, 52. hempel, 53. otto, 54. boulding, 55. aries, 56. driver, 57. cressey, 58. lindblom, 59. shepard, 60. goldberger, 61. centers, 62. gurr, 63. knoke, 64. oberschall, 65. rapoport, 66. dohrenwend, 67. cain, 68. coale, 69. lyman, 70. dumont, 71. goody, 72. kish, 73. rotter, 74. wolfgang, 75. westoff, 76. gurin, 77. hanushek, 78. nie, 79. braverman, 80. land, 81. levinson, 82. murdock, 83. hammond, 84. lowenthal, 85. neugarten, 86. lester, 87. child, 88. lenin, 89. form, 90. cantril, 91. taeuber, 92. poulantzas, 93. featherman, 94. freedman, 95. rossi, 96. hauser, 97. mueller, 98. wirth, 99. schuman
34425 total
0. kornai, 1. scully, 2. neter, 3. abercrombie, 4. cleary, 5. liebman, 6. crandall, 7. fararo, 8. mouzelis, 9. kreps, 10. staples, 11. bahr, 12. nelsen, 13. aldous, 14. sudman, 15. wiley, 16. box, 17. friedan, 18. silberman, 19. press, 20. odonnell, 21. pollak, 22. abrahamson, 23. dore, 24. london, 25. coulter, 26. ashworth, 27. willmott, 28. keyfitz, 29. clegg, 30. dornbusch, 31. mott, 32. cantor, 33. roemer, 34. rollins, 35. obrien, 36. maccoby, 37. straus, 38. sweet, 39. perrow, 40. labov, 41. blumstein, 42. rothman, 43. summers, 44. herrnstein, 45. handler, 46. cockburn, 47. rosaldo, 48. polachek, 49. mehan, 50. delacroix, 51. chafetz, 52. roos, 53. eisenstein, 54. trivers, 55. gelles, 56. bane, 57. spenner, 58. iso-ahola, 59. deem, 60. felmlee, 61. pollert, 62. fossett, 63. lennon, 64. grossberg, 65. szelenyi, 66. skvoretz, 67. spitze, 68. parcel, 69. clogg, 70. marini, 71. maddala, 72. chodorow, 73. kluegel, 74. oakley, 75. piore, 76. roof, 77. stolzenberg, 78. thurow, 79. pleck, 80. csikszentmihaly.m, 81. menaghan, 82. coverman, 83. markovsky, 84. demo, 85. lyotard, 86. yamaguchi, 87. gilligan, 88. gove, 89. hannan, 90. aldrich, 91. kanter, 92. farley, 93. berk, 94. pahl, 95. tuma, 96. dollard, 97. hiller, 98. herskovits, 99. lieberson
36105 total
0. spain, 1. boden, 2. beatty, 3. miethe, 4. rook, 5. billy, 6. crouter, 7. samdahl, 8. beggs, 9. aseltine, 10. ascione, 11. mauthner, 12. fuligni, 13. chantala, 14. astone, 15. lehrer, 16. hammitt, 17. reinharz, 18. tickamyer, 19. steelman, 20. sabo, 21. arluke, 22. coffey, 23. mcdonough, 24. lyson, 25. isoahola, 26. waldron, 27. cernkovich, 28. shelby, 29. delphy, 30. spender, 31. wagenaar, 32. picou, 33. hedges, 34. acheson, 35. kotler, 36. rosenstone, 37. mcallister, 38. donaldson, 39. dickens, 40. nelkin, 41. poster, 42. knights, 43. mchale, 44. hanks, 45. mackay, 46. himmelfarb, 47. blaug, 48. spilka, 49. kain, 50. kagan, 51. psathas, 52. geis, 53. rieff, 54. grimshaw, 55. beattie, 56. schulze, 57. agger, 58. pollard, 59. munch, 60. donahue, 61. hecht, 62. crick, 63. gorz, 64. rappaport, 65. wrigley, 66. jessor, 67. mulkay, 68. fogel, 69. ewen, 70. bosk, 71. hagenaars, 72. glassner, 73. heelas, 74. ogbu, 75. hebdige, 76. corcoran, 77. maines, 78. hirst, 79. thorne, 80. rutter, 81. seale, 82. mosse, 83. gubrium, 84. tienda, 85. bryk, 86. shilling, 87. bordo, 88. featherstone, 89. kasarda, 90. astin, 91. kay, 92. hanson, 93. winter, 94. haines, 95. vandijk, 96. titmuss, 97. johnstone, 98. mccormick, 99. maynard
341957 total
0. green, 1. edwards, 2. robinson, 3. hill, 4. harris, 5. allen, 6. atkinson, 7. peterson, 8. douglas, 9. cook, 10. baker, 11. nelson, 12. blumer, 13. morris, 14. hunter, 15. schneider, 16. fox, 17. friedman, 18. jenkins, 19. bennett, 20. rogers, 21. simon, 22. rosenberg, 23. schwartz, 24. strauss, 25. west, 26. erikson, 27. gordon, 28. mills, 29. katz, 30. alexander, 31. lewis, 32. bell, 33. merton, 34. campbell, 35. miller, 36. hall, 37. brown, 38. cohen, 39. wilson, 40. becker, 41. taylor, 42. thompson, 43. jackson, 44. hughes, 45. young, 46. marx, 47. shaw, 48. marshall, 49. walker, 50. king, 51. white, 52. lee, 53. turner, 54. davis, 55. anderson, 56. duncan, 57. adams, 58. butler, 59. james, 60. ross, 61. beck, 62. de, 63. van, 64. rose, 65. collins, 66. scott, 67. meyer, 68. harvey, 69. glaser, 70. allison, 71. sampson, 72. connell, 73. putnam, 74. gamson, 75. stark, 76. habermas, 77. jacobs, 78. snow, 79. evans, 80. bauman, 81. tilly, 82. portes, 83. kim, 84. hochschild, 85. granovetter, 86. massey, 87. dimaggio, 88. latour, 89. foucault, 90. giddens, 91. berger, 92. blau, 93. coleman, 94. goffman, 95. goodman, 96. garfinkel, 97. geertz, 98. bourdieu, 99. smith
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