=Paper=
{{Paper
|id=Vol-3834/paper41
|storemode=property
|title=A quantitative study of gender representation and authors' gender in a large-market print medium
|pdfUrl=https://ceur-ws.org/Vol-3834/paper41.pdf
|volume=Vol-3834
|authors=Christoph Bartl,Sharwin Rezagholi,Mareike Schumacher
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==A quantitative study of gender representation and authors' gender in a large-market print medium==
https://ceur-ws.org/Vol-3834/paper41.pdf
A quantitative study of gender representation and
authors’ gender in a large-market print medium⋆
Christoph Bartl1 , Sharwin Rezagholi2,∗ and Mareike Schumacher3
1
Department Computer Science, University of Applied Sciences Technikum Wien, Austria
2
Department Computer Science, University of Applied Sciences Technikum Wien, Austria
3
Institute of Literary Studies, University of Stuttgart, Germany
Abstract
We analyse gender representation in articles published by the Austrian daily newspaper ’Der Standard’
in the years 2021 and 2022. We use named entity recognition and automated gender classification of first
names to count the number of female and male persons in articles. The analysis reveals the dominance
of male persons in article content. We find that female authors exhibit a significantly higher tendency
to mention female persons in their articles.
Keywords
Gender of journalists, gender representation in articles, print newspaper content
1. Introduction
This paper asks whether (i) female persons are less likely to be mentioned by newspaper jour-
nalists than male persons, and (ii) whether the propensity to mention female persons differs
between female and male journalists. To answer these questions we analyse the entire article
output of the Austrian daily newspaper Der Standard (https://www.derstandard.at) from the
years 2021 and 2022. These articles are publicly available online. We additionally obtained the
full names of the authors of all articles from Der Standard; this authorship information is non-
public for most of the articles. Der Standard is the fourth-largest daily newspaper in Austria,
having more than 500,000 daily readers [29].
This study employs a binary notion of gender, as does the language policy of Der Standard,
which prescribes the sole use of feminine and masculine pronouns, effectively prohibiting the
use of neopronouns. Therefore this study does not contribute to the abolishment of a binary
notion of gender, an aim prominently championed within the digital humanities by Laura Man-
dell [20].
We employ pretrained models for natural language processing to automatically identify and
enumerate female and male persons mentioned in article texts. In particular, we use named
entity recognition and automated gender-assignment to first names. The respective methods
CHR 2024: Computational Humanities Research Conference, December 4–6, 2024, Aarhus, Denmark
∗
Corresponding author.
£ christoph.bartl@me.com (C. Bartl); sharwin.rezagholi@technikum-wien.at (S. Rezagholi);
mareike.schumacher@ur.de (M. Schumacher)
ȉ 0000-0003-1090-0240 (S. Rezagholi); 0000-0002-7952-4194 (M. Schumacher)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
1037
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
are susceptible to bias. We therefore estimate the error profile of the gender assignment to first
names and verify that there is no significant bias that might have distorted our analysis.
We statistically estimate the probability that a journalist mentions a female person when
mentioning a person. We find that females are less likely to be mentioned by journalists. This
finding holds for male as well as female authors, but we find it to be less pronounced in female
journalists. These effects are present to different degrees in different editorial departments,
with some departments not exhibiting the imbalance at all. The causal pathways leading to
these effects could include statistical ’self-selection’, whereby female authors and male authors
have a differing propensity to report on certain issues, or female authors could tend to highlight
the roles of female persons more than their male colleagues, even when reporting on the same
issue.
Literature review. The issue of gender representation and gender inequality is lively dis-
cussed in the digital humanities, including computational linguistics [12], digital film studies
[3, 32], game studies [31], and computational literary studies [4, 5, 8, 13, 19, 30, 32]. Newspa-
pers and magazines in particular have been studied with respect to gender representation and
possible gender bias. Yun et al. found that women were given more space in online than in
print journals and that, in a significant fraction of cases, women were portrayed in stereotyp-
ical ways [16]. Kian et al. analysed tennis news, finding that female reporters did not write
more often about female athletes than their male colleagues but that female reporters tended
to use more stereotypical descriptions [17]. Kozlowski et al. [18] analyse the magazines from
an Argentinian publisher from 2008 to 2018 using topic modelling and find that the prevalence
of thematic areas differs between magazines that target female readers and those that target
male ones. They find that this gap is diminishing with respect to certain topics, such as ’family’
and ’children’, whereas it remains large in others, such as ’fashion’ and ’horoscope’. The large-
scale analysis of Shor et al. [27], in which more than 20,000 prominent personalities of male and
female gender and different (but matching) professions were searched in about 2,000 English-
language newspapers, came to the conclusion that the reduced media coverage of women is
not in line with the readers’ interest, which does not favour prominent men over prominent
women. They thus provide some evidence in favour of the hypothesis that newspapers and
magazines foster stereotypes and gender bias.
The work most related to ours is due to Mateos de Cabo et al. [21], whose analysis of Spanish
online newspapers found that females were more likely to be mentioned in female-authored
articles, and to Shor et al. [28], whose analysis of about 2,000 news sources found that the
fraction of females in articles increased from 19% in 1983 to 27% in 2008. The latter also found
significant differences between editorial departments.
The digital humanities community has formulated a need to further the application of its
methods to questions of gender. These voices include Miriam Posner [23], who criticized that
gender-related work in the digital humanities does not receive sufÏcient attention, neither from
the scholarly community nor from news outlets. In 2018 Susan Brown stated that a feminist
perspective is largely lacking in the digital humanities, going as far as calling ’feminism’ the ’f
word’, suggesting that feminist approaches are effectively silenced [6]. In 2019 Laura Mandell
argued that studies on gender within the digital humanities would rather reproduce stereotypes
1038
than analyse them [20]. Coining the term ’data feminism’ in 2020, D’Ignazio and Klein drew
awareness to gender representation biases in the digital humanities and in other data-driven
fields [10]. Although gender bias has been studied in various domains [9, 25, 15, 30, 19, 11, 24,
12], we agree that a sufÏcient corpus of statistical results remains absent.
2. Data and methods
We start with the text and metadata of 87,032 articles, corresponding to the entire journalistic
output of Der Standard between January 1, 2021 and December 31, 2022. The metadata of the
articles includes authorship, publication date, title, and editorial department. We removed all
articles from consideration that were written by a group of journalists or signed by a press
agency. This restriction is in line with our interest in the behaviour of individual writers at
a newspaper, as opposed to press agencies, group authorship, or anonymous authorship. We
retain 36,204 articles.
Named entity recognition and gender-assignment. We use the Python package Gender-
Guesser 0.4.0 to assign gender to the authors’ given names. GenderGuesser uses a database
of 40,000 gender-assigned first names to assign gender to a given name [2]. Since some given
names, such as ‘Andrea’, ’Maria’ or ‘Robin’, are not gender-exclusive, GenderGuesser returns
‘mostly female’, ‘mostly male’, and ‘androgynous’ in some cases. We assigned the first two
of these categories to ‘female’ and ‘male’ respectively. We manually checked the gender-
assignments of all 1,571 authors and corrected three cases. We therefore treat the assignment
of gender to the names of authors as certainly correct in the remainder of our analysis.
We used the Python package Flair 0.12.2 [1] to recognize personal names in the article texts
using the named entity recognition model ‘ner-german-large’ [26]. In a first step we restrict
attention to names consisting of a given name and a family name since the editorial policy
of Der Standard prescribes the use of the full name at least once per article. The gender of
the detected given names was then determined using GenderGuesser. Names such as ‘Barack
Obama’, ‘Viktor F.’, ‘Angela Merkel’, ‘Nina H.’, and ’Luke Skywalker’ were identified. In a
second step we parse the articles for mentions of the identified persons using only a part of the
full name. To clarify our counting method: An article mentioning two persons, the same male
person 9 times and a female person once, is considered a text where 90% of mentioned persons
are male.
We evaluated our extraction of full names in comparison to the manual counting of the num-
ber of female and male full names in 200 randomly selected articles (Table 2). We use binomial
estimates to quantify the conditional accuracies of the automated extraction (Table 1). The fair-
ness criterion of predictive parity requires the equality of the true positive rate for male and
female cases [7]. We find that these two rates are numerically very similar, approximately 0.87
and 0.88, and that the hypothesis that they are equal can not be rejected (Binomial proportions
test, 𝑝 = 0.68).
Generative model. Our main interest is the probability that an author, when mentioning a
person, does mention a female person. Our task is complicated by the fact that the automated
1039
Table 1
Performance of gendered name recognition.
Estimate St. dev. 95% interval (Wald)
ℙ(classified as male|male) 0.869 0.013 [0.844, 0.893]
ℙ(classified as female|female) 0.881 0.026 [0.831, 0.931]
ℙ(classified as female|male) 0.003 0.002 [0.000, 0.007]
ℙ(classified as male|female) 0.019 0.010 [0.000, 0.040]
ℙ(not detected|male) 0.129 0.013 [0.104, 0.153]
ℙ(not detected|female) 0.100 0.024 [0.054, 0.146]
0.88
name is female detected as female
p 0.10
0.02
name mentioned name not detected
0.13
0.00
1−p
0.87
name is male detected as male
Figure 1: Generative model. Arrows are labelled with estimated conditional probabilities computed
on the basis of the manually labelled test set (Table 2).
detection of full names and the automated assignment of gender to the respective first names
could be biased. The probability that we have access to is
ℙ(detected as female|name detected).
We consider a simple generative model for our data (Figure 1), which illustrates that the events
’detected as female’ and ’detected as male’ do not allow the identification of the parameter of
interest, that is 𝑝, unless certain assumptions are made. The two necessary assumptions are
the absence of mix-ups, that is
ℙ(detected as female|male) = ℙ(detected as male|female) = 0, (1)
and unbiased non-detection, that is
ℙ(not detected|female) = ℙ(not detected|male). (2)
If Equations 1 and 2 hold, then
ℙ(fem. name detected|name detected) = ℙ(fem. name mentioned|name mentioned).
1040
Table 2
Contingency table for gendered name recognition.
Classified as male Classified as female Not recognized Total
Male 621 2 92 715
Female 3 141 16 160
Total 624 143 108
It is not necessary to assume that non-detection does not occur, but that non-detection is un-
biased. The empirical probabilities corresponding to those in Equation 2 are similar, approx-
imately 0.10 and 0.13, and the hypothesis that they are equal can not be rejected (Binomial
proportions test, 𝑝 = 0.31). As Table 1 reports, the empirical probabilities for mix-ups equal
approximately 0.00 and 0.02. We feel that these values are sufÏciently low to assume that Equa-
tion 1 holds. In Table 2 the fraction of female persons among the mentioned persons equals
160/(160 + 715) ≈ 0.18 while the fraction of names classified as female among the classified
names equals 143/(143 + 624) ≈ 0.19. This numerically illustrates the absence of bias.
Statistical model. The details of our statistical approach are presented in Appendix A. Our
basic modelling assumption is that the number of persons in an article is predetermined, but
the respective journalist ’chooses’ the gender of the mentioned persons independently from a
Bernoulli distribution. The parameter of this Bernoulli distribution is specific to the subset of
the data for which an estimate is desired. We believe that this model is sufÏcient to organise
the data in a tractable and intuitive fashion. To be concrete: We estimate the probability that a
journalist uses the name of a female person when using the name of a person. Note that our es-
timate does not equal the fraction of female persons in a certain subset of articles. Our estimate
is descriptive of authors’ behaviour, not of article output (see Appendix A). It is important to
note that every author, regardless of the number of persons mentioned in their respective arti-
cles, has equal importance for our point estimates, but the differing degrees of uncertainty for
different authors, caused by the different quantities of mentioned persons, are reflected in the
interval estimates, that is in the confidence intervals. When we estimate probabilities specific
to editorial departments, we employ a weighting scheme whereby the degree of membership
of an author in an editorial department is taken into account. Consult Appendix A for details
on our statistical approach.
3. Descriptive analysis
Of the 36,204 articles, 12,736 (35%) were written by a female author and 23,468 (65%) were
written by a male author. Among the 1,571 unique authors 606 (39%) are female. The average
author has contributed 23 articles to the dataset. The articles are of varying length, the median
length being 3,859 characters (1st quartile = 2,560, 3rd quartile = 5,248).
The mean of the fraction of females in an article equals 25%. The respective mean for the arti-
cles written by male writers equals 20%. On the other hand, the mean for the articles authored
1041
1.00 1.00 1.0
Fraction of female persons in article
Fraction of female persons in article
Fraction of female persons in article
0.6
0.43
0.33
0.2
0.07
0.00 0.00 0.0
All articles Male-authored articles Female-authored articles
Figure 2: Empirical distributions of the fraction of females in an article, quartiles on the vertical axes.
by female writers equals 33%. The empirical distributions of the fraction of female persons
in an article are visualized in Figure 2. Note that we only consider articles that do mention
at least one person. It is apparent that many articles that do mention persons do not mention
females at all. This is true for female- as well as male-authored articles. It is evident that the dis-
tributions for female- and male-authored articles differ. The distribution for female-authored
articles stochastically dominates the distribution for male-authored articles (McFadden’s test
[22], 𝑝 = 0.00). The distributions exhibit concentrations at the extremes. This illustrates that
many articles solely mention persons of one gender.
Differentiating with respect to the editorial departments of Der Standard (Table 3), one finds
that the department Family produces the smallest number of articles but has the largest fraction
of female authorship. The largest number of articles is produced by the department Culture,
which corresponds to roughly 13% of our data.
4. Statistical estimation
Our first interests are to estimate the probabilities that (i) an author who mentions a person
does mention a female person, (ii) a female author who mentions a person does mention a
female person, and (iii) a male author who mentions a person does mention a female person.
The respective estimated probabilities are reported in Table 4 and visualised in Figure 3. While
the probability that an author mentions a female person when mentioning a person is estimated
to be roughly 27%, the respective estimate for female authors is roughly 46% and roughly 14%
for male authors. The differences are highly statistically significant (𝑝 = 0.00). We conclude
that there is strong evidence, at least in our data, that female journalists are more likely to
mention female persons in their articles compared to male journalists.
To elucidate the differences between authors from different editorial departments, we esti-
mate the probability that a journalist from a given department mentions a female person when
mentioning a person. These estimates are reported in Table 5 and visualized in Figure 4. There
are editorial departments whose output is likely to mention females, such as Female Standard
and Family, and departments whose writers are unlikely to mention females, such as Automo-
1042
Table 3
Data composition and author’s gender in terms of editorial departments.
Department Article Article Female Fraction Unique Unique Fraction
count per- author- female au- female female
cent ship thors au-
count thors
Wirtschaft
3131 8.65 1462 0.47 134 44 0.33
(Economy)
Karriere
724 2.00 584 0.81 78 40 0.51
(Career)
Recht
704 1.94 148 0.21 131 36 0.27
(Law)
Die Standard
367 1.01 335 0.91 54 46 0.85
(Female Standard)
Gesundheit
657 1.81 603 0.92 47 30 0.64
(Health)
Automobil
644 1.78 44 0.07 35 9 0.26
(Automobile)
Web 3734 10.31 44 0.01 46 9 0.20
Reisen
319 0.88 140 0.44 52 23 0.44
(Travel)
Meinung
4205 11.61 1472 0.35 692 226 0.33
(Opinion)
International
3812 10.53 1360 0.36 150 54 0.36
(International)
Inland
1972 5.45 638 0.32 88 41 0.47
(Domestic)
Lifestyle 1588 4.39 833 0.52 112 60 0.54
Etat
1902 5.25 716 0.38 98 37 0.38
(Government)
Immobilien
853 2.36 364 0.43 39 17 0.44
(Realty)
Bildung
466 1.29 335 0.72 64 38 0.59
(Education)
Zukunft
628 1.73 145 0.23 44 25 0.57
(Future)
Sport 1375 3.80 28 0.02 61 14 0.23
Familie
238 0.66 230 0.97 32 25 0.78
(Family)
Kultur
4625 12.77 1691 0.37 294 131 0.45
(Culture)
Panorama 2330 6.44 858 0.37 177 70 0.40
Wissenschaft
1930 5.33 706 0.37 156 80 0.51
(Science)
1043
All authors
Male authors
Female authors
0 0.14 0.27 0.46 1
Probability that author mentions a female person when mentioning a person
Figure 3: Estimated probabilities that an author, when mentioning a person, does mention a female
person (with 95%-confidence intervals).
Opinion
International
Domestic
Career
Government
Female Standard
Health
Science
Economy
Sport
Family
Panorama
Realty
Law
Culture
Future
Lifestyle
Travel
Web
Education
Automobile
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Probability that author mentions a female person when mentioning a person
Figure 4: Estimated probability that an author from the respective department mentions a female
person when mentioning a person (with 95%-confidence intervals).
bile and Sport. These findings are in line with previous studies from different countries and
languages [17, 28, 18].
To disentangle the effect of authors’ gender and editorial departments, we stratify our anal-
ysis with respect to both. These estimates are visualized in Figure 5 and reported in Table 6,
including hypothesis tests for the null-hypothesis that female and male authors behave identi-
cally. We obtain significantly different estimates for female and male authors for many edito-
1044
Opinion, fem. auth.
Opinion, male auth.
International, fem. auth.
International, male auth.
Domestic, fem. auth.
Domestic, male auth.
Career, fem. auth.
Career, male auth.
Government, fem. auth.
Government, male auth.
Female Standard, fem. auth.
Female Standard, male auth.
Health, fem. auth.
Health, male auth.
Science, fem. auth.
Science, male auth.
Economy, fem. auth.
Economy, male auth.
Sport, fem. auth.
Sport, male auth.
Family, fem. auth.
Family, male auth.
Panorama, fem. auth.
Panorama, male auth.
Realty, fem. auth.
Realty, male auth.
Law, fem. auth.
Law, male auth.
Culture, fem. auth.
Culture, male auth.
Future, fem. auth.
Future, male auth.
Lifestyle, fem. auth.
Lifestyle, male auth.
Travel, fem. auth.
Travel, male auth.
Web, fem. auth.
Web, male auth.
Education, fem. auth.
Education, male auth.
Automobile, fem. auth.
Automobile, male auth.
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Probability that author mentions a female person when mentioning a person
Figure 5: Estimated probability that a journalist from the respective department and of the respective
gender mentions a female person when mentioning a person (with 95%-confidence intervals).
rial departments. The departments Opinion, Career, Female Standard, Science, Economy, Law,
Culture, Lifestyle, Web, and Automobile exhibit highly significant differences with respect to
author gender (𝑝 = 0.00). The departments Domestic, Government, Health, Realty, Travel, and
Education do not exhibit statistically significant gender differences (𝑝 > 0.1).
1045
5. Conclusion and caveats
We present some statistical evidence for the hypothesis that female and male journalists have
differing propensities to mention female persons in their writing. This effect varies between
editorial departments, at least in our data, and is stronger within certain editorial departments
than across editorial departments. Further research is needed to elucidate whether the find-
ings of the present study are driven by a mechanism through which female journalists write
about different topics than male journalists. Even if this were true, it would remain ambiguous
whether the ultimate cause is the issue-assignment policy within newsrooms, or a desire by
journalists to write on topics featuring persons of a certain gender. The latter could also corre-
spond to a conscious attempt by female journalists to highlight female persons in an attempt to
counteract existing gender imbalances. We deem it an interesting avenue for further research
to quantitatively elucidate the relationship between journalistic topics, gender representation,
and authorship. If the observed differences were caused by different propensities in mentioning
female persons even when reporting on the same topic, this would indicate gender-specificity
in journalist’s viewpoints. Finally we want to highlight that the present study is most cer-
tainly marred by the problem that many relevant and potentially confounding factors, such as
topics, have not yet been taken into account. Therefore the present study is but an empirical
quantification of a status quo.
Acknowledgments
The authors thank Martin Kotynek and Werner Weichselberger from Der Standard for their
support.
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des Computerspiels. 2021.
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A. Appendix
Maximum likelihood estimate. We consider the weighted likelihood
𝑙 𝑤𝑖
𝑘
𝐿(𝑝1 , ..., 𝑝𝑙 ) ∝ ∏ (∏ (𝑝𝑗 𝑖 (1 − 𝑝𝑗 )𝑚𝑖 −𝑘𝑖 ) ) , (3)
𝑗=1 𝑖∈𝑆𝑗
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where 𝑘𝑖 denotes the number of persons detected as female in article 𝑖, 𝑚𝑖 denotes the number
of detected persons in article 𝑖, {𝑆1 , ..., 𝑆𝑙 } are disjoint subsets of the data {1, ..., 𝑛}, and the weight
𝑤𝑖 equals the reciprocal of the number of natural persons detected in articles written by the
respective journalist 𝑎𝑖 , that is
1
𝑤𝑖 = . (4)
∑𝑗∶𝑎𝑖 =𝑎𝑗 𝑚𝑗
Note that we have discarded multiplicative constants from Equation 3. Weighted likelihood
estimation is a well-established method in several circumstances [14]. The likelihood (Equation
3) is maximized at the parameter-values {𝑝1̂ , ..., 𝑝𝑙̂ } given by
∑𝑖∈𝑆𝑗 𝑤𝑖 𝑘𝑖
𝑝𝑗̂ = . (5)
∑𝑖∈𝑆𝑗 𝑤𝑖 𝑚𝑖
Under our choice of weighting (Equation 4), the maximum-likelihood estimates according to
Equation 5 can be written as
𝑘
∑𝑎∈𝐴 𝑚𝑎,𝑗 𝑚𝑎,𝑗 𝑘𝑎,𝑗
𝑎 1
𝑝𝑗̂ = 𝑚 = 𝑚𝑎,𝑗 ∑ ,
∑𝑎∈𝐴 𝑚𝑎,𝑗 ∑𝑎∈𝐴 𝑚 𝑎∈𝐴 𝑚𝑎 𝑚𝑎,𝑗
𝑎 𝑎
where 𝐴 denotes the set of unique authors, 𝑚𝑎,𝑗 denotes the number of persons detected in
texts of author 𝑎 in subset 𝑆𝑗 , 𝑚𝑎 denotes the number of persons detected in texts of author
𝑎, and 𝑘𝑎,𝑗 denotes the number of female persons detected in articles by author 𝑎 in subset 𝑆𝑗 .
This is but the weighted mean of the naive per-author estimates for the subset, that is 𝑘𝑎,𝑗 /𝑚𝑎,𝑗 ,
weighted by the ’degree of membership’ of author 𝑎 in subset 𝑆𝑗 , that is by 𝑚𝑎,𝑗 /𝑚𝑎 . Note that
this estimator is such that multiplying all data from a certain author by a constant does not
change the estimate. In the special case of a single subset equal to the entirety of the data, the
estimator takes the form
1 ∑𝑖∶𝑎𝑖 =𝑎 𝑘𝑖
𝑝̂ = ∑( ),
|𝐴| 𝑎∈𝐴 ∑𝑖∶𝑎𝑖 =𝑎 𝑚𝑖
which is but the arithmetic average of the per-author relative frequencies.
Confidence intervals. The variance of 𝑝𝑗̂ equals
1
𝑉 (𝑝𝑗̂ ) = 2
∑ 𝑤𝑖2 𝑉 (𝑘𝑖 )
( ∑𝑖∈𝑆𝑗 𝑤𝑖 𝑚𝑖 ) 𝑖∈𝑆𝑗
where 𝑘𝑖 ∼ binomial(𝑚𝑖 , 𝑝𝑗 ) and hence 𝑉 (𝑘𝑖 ) = 𝑝𝑗 (1 − 𝑝𝑗 )𝑚𝑖 . Therefore the plug-in estimator
for the variance of 𝑝𝑗̂ is
∑𝑖∈𝑆𝑗 𝑤𝑖2 𝑚𝑖
𝑉 (𝑝𝑗̂ ) ≈ 𝑝𝑗̂ (1 − 𝑝𝑗̂ ) 2
(6)
( ∑𝑖∈𝑆𝑗 𝑤𝑖 𝑚𝑖 )
This enables us to use a normal approximation to the distribution of 𝑝𝑗̂ to construct confidence
intervals.
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Hypothesis tests. To test null-hypotheses of the form 𝑝𝑗 = 𝑝𝑗 ′ , we construct a test using the
test statistic
𝑝𝑗̂ − 𝑝𝑗̂ ′ ∼ 𝑁 (𝑝𝑗̂ − 𝑝𝑗̂ ′ , 𝑉 (𝑝𝑗̂ ) + 𝑉 (𝑝𝑗̂ ′ )),
where the variances are computed according to Equation 6.
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Table 4
Estimated probabilities that an author, when mentioning a person, does mention a female person.
Model Article count Estimate St. dev. 95% interval
All authors 30,099 0.265 0.006 [0.254,0.277]
Male authors 19,444 0.141 0.006 [0.129,0.152]
Female authors 10,655 0.464 0.011 [0.444,0.485]
Table 5
Estimated department-specific probability that an author mentions a female person when mentioning
a person.
Model Article count Estimate St. dev. 95% interval
Opinion 3293 0.241 0.011 [0.218,0.263]
International 3577 0.203 0.013 [0.178,0.229]
Domestic 1934 0.172 0.010 [0.152,0.191]
Career 502 0.305 0.048 [0.211,0.399]
Government 1780 0.265 0.032 [0.202,0.328]
Female Standard 312 0.759 0.035 [0.691,0.827]
Health 577 0.333 0.023 [0.289,0.378]
Science 1803 0.308 0.021 [0.267,0.348]
Economy 2858 0.185 0.016 [0.153,0.217]
Sport 1333 0.136 0.014 [0.108,0.163]
Family 139 0.514 0.081 [0.355,0.674]
Panorama 2062 0.256 0.023 [0.211,0.302]
Realty 685 0.174 0.030 [0.115,0.233]
Law 374 0.204 0.040 [0.124,0.283]
Culture 4400 0.313 0.012 [0.289,0.337]
Future 484 0.318 0.050 [0.220,0.417]
Lifestyle 1138 0.355 0.021 [0.313,0.396]
Travel 161 0.274 0.058 [0.159,0.388]
Web 1960 0.152 0.024 [0.104,0.200]
Education 390 0.402 0.056 [0.293,0.511]
Automobile 337 0.066 0.037 [0.000,0.138]
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Table 6
Estimated department- and gender-specific probabilities that a journalist mentions a female person
when mentioning a person.
Model Art. ct. Estimate St. dev. 95% interval p-value, 𝐻0 ∶ 𝑝𝑚 = 𝑝𝑓
Opinion, female authors 1030 0.579 0.025 [0.530,0.628] 0.00
Opinion, male authors 2263 0.093 0.009 [0.075,0.110]
International, female authors 1279 0.234 0.024 [0.187,0.281] 0.09
International, male authors 2298 0.186 0.015 [0.157,0.216]
Domestic, female authors 631 0.173 0.019 [0.135,0.211] 0.92
Domestic, male authors 1303 0.171 0.009 [0.154,0.188]
Career, female authors 398 0.600 0.064 [0.475,0.724] 0.00
Career, male authors 104 0.060 0.037 [0.000,0.133]
Government, female authors 662 0.340 0.066 [0.210,0.470] 0.11
Government, male authors 1118 0.220 0.034 [0.154,0.287]
Female Standard, female authors 289 0.780 0.035 [0.711,0.850] 0.00
Female Standard, male authors 23 0.362 0.048 [0.267,0.456]
Health, female authors 526 0.329 0.025 [0.280,0.378] 0.82
Health, male authors 51 0.341 0.045 [0.252,0.429]
Science, female authors 673 0.443 0.031 [0.382,0.503] 0.00
Science, male authors 1130 0.134 0.022 [0.091,0.177]
Economy, female authors 1332 0.258 0.026 [0.207,0.310] 0.00
Economy, male authors 1526 0.155 0.020 [0.117,0.194]
Sport, female authors 23 0.064
Sport, male authors 1310 0.141 0.011 [0.120,0.162]
Family, female authors 134 0.543 0.091 [0.364,0.722]
Family, male authors 5 0.309
Panorama, female authors 766 0.336 0.052 [0.234,0.437] 0.03
Panorama, male authors 1296 0.214 0.023 [0.168,0.260]
Realty, female authors 271 0.135 0.029 [0.079,0.191] 0.25
Realty, male authors 414 0.200 0.048 [0.106,0.294]
Law, female authors 76 0.630 0.083 [0.468,0.792] 0.00
Law, male authors 298 0.036 0.023 [0.000,0.080]
Culture, female authors 1591 0.432 0.019 [0.394,0.470] 0.00
Culture, male authors 2809 0.217 0.015 [0.188,0.245]
Future, female authors 93 0.423 0.068 [0.289,0.558] 0.01
Future, male authors 391 0.169 0.064 [0.043,0.295]
Lifestyle, female authors 537 0.455 0.033 [0.390,0.520] 0.00
Lifestyle, male authors 601 0.238 0.024 [0.191,0.286]
Travel, female authors 40 0.351 0.072 [0.210,0.491] 0.19
Travel, male authors 121 0.207 0.084 [0.043,0.372]
Web, female authors 21 0.374 0.063 [0.251,0.497] 0.00
Web, male authors 1939 0.111 0.024 [0.064,0.159]
Education, female authors 268 0.360 0.060 [0.243,0.477] 0.40
Education, male authors 122 0.462 0.105 [0.256,0.669]
Automobile, female authors 15 0.646 0.072 [0.505,0.787] 0.00
Automobile, male authors 322 0.060 0.035 [0.000,0.129]
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