Public Health, Private Parts: A Feminist Public-Health Approach to Trans Issues

Multilingual Wikipedia

In June 2020 the work Public Health, Private Parts: A Feminist Public-Health Approach to Trans Issues was on the 5,370th place in the ranking of the most reliable and popular publications with DOI number in multilingual Wikipedia from readers' point of view (PR-score). If we consider only frequency of appearance of this source in references of Wikipedia articles (F-score), this work was on the 87,535th place in June 2020. From Wikipedians' point of view, "Public Health, Private Parts: A Feminist Public-Health Approach to Trans Issues" is the 74,634th most reliable publication with DOI number in different language versions of Wikipedia (AR-score).

PR-score:
5,370th place
214,320
+65,033
AR-score:
74,634th place
2,989
+17
F-score:
87,535th place
9
0

English Wikipedia (en)

PR-score:
8,667th place
121,213
+43,253
AR-score:
98,607th place
1,847
+3
F-score:
410,732nd place
1
0

Russian Wikipedia (ru)

PR-score:
1,831st place
35,070
+11,355
AR-score:
28,639th place
135
0
F-score:
48,066th place
1
0

Spanish Wikipedia (es)

PR-score:
4,379th place
30,975
+6,039
AR-score:
21,401st place
315
+4
F-score:
51,469th place
1
0

Hungarian Wikipedia (hu)

PR-score:
190th place
10,771
+529
AR-score:
1,625th place
357
0
F-score:
11,165th place
1
0

Portuguese Wikipedia (pt)

PR-score:
3,903rd place
9,142
+1,814
AR-score:
20,610th place
123
+5
F-score:
26,744th place
1
0

Chinese Wikipedia (zh)

PR-score:
10,922nd place
4,500
+1,045
AR-score:
36,377th place
61
0
F-score:
45,991st place
1
0

Hebrew Wikipedia (he)

PR-score:
977th place
2,118
+878
AR-score:
3,189th place
122
+4
F-score:
3,663rd place
1
0

Ukrainian Wikipedia (uk)

PR-score:
6,245th place
522
+121
AR-score:
24,021st place
7
0
F-score:
23,228th place
1
0

Asturian Wikipedia (ast)

PR-score:
5,880th place
8
+2
AR-score:
3,846th place
18
0
F-score:
6,592nd place
1
0

BestRef shows popularity and reliability scores for sources in references of Wikipedia articles in different languages. Data extraction based on complex method using Wikimedia dumps. To find the most popular and reliable sources we used information about over 200 million references of Wikipedia articles. More details...