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Over twenty years of publications in Ecology: Over-contribution of women reveals a new dimension of gender bias
Women overcompensation in ecology publishing
https://orcid.org/0000-0001-5842-2263
Fontanarrosa Gabriela Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Visualization Writing – original draft Writing – review & editing 1
https://orcid.org/0000-0002-9118-0566
Zarbá Lucía Conceptualization Data curation Formal analysis Investigation Methodology Writing – original draft Writing – review & editing 2
https://orcid.org/0000-0003-3865-4133
Aschero Valeria Conceptualization Data curation Formal analysis Investigation Methodology Writing – original draft Writing – review & editing 3
Dos Santos Daniel Andrés Conceptualization Formal analysis Investigation Methodology Supervision Writing – review & editing 1 4
Nuñez Montellano María Gabriela Conceptualization Data curation Formal analysis Funding acquisition Investigation Project administration Writing – review & editing 5
Plaza Behr Maia C. Conceptualization Data curation Formal analysis Investigation Project administration Writing – review & editing 5
Schroeder Natalia Conceptualization Data curation Investigation Writing – original draft Writing – review & editing 6 7
Lomáscolo Silvia Beatriz Conceptualization Data curation Formal analysis Project administration Writing – review & editing 5
https://orcid.org/0000-0003-3061-7725
Fanjul María Elisa Data curation Investigation Project administration Writing – review & editing 4 8
https://orcid.org/0000-0003-0240-719X
Monmany Garzia A. Carolina Data curation Writing – review & editing 5
Alvarez Marisa Conceptualization Data curation Investigation Writing – review & editing 9 10
Novillo Agustina Data curation Formal analysis Investigation Writing – review & editing 1
Lorenzo Pisarello María José Data curation Investigation Writing – review & editing 11
D’Almeida Romina Elisa Data curation Writing – review & editing 12
Valoy Mariana Data curation Writing – review & editing 8
Ramírez-Mejía Andrés Felipe Data curation Formal analysis Investigation Methodology Writing – review & editing 5
Rodríguez Daniela Data curation Writing – review & editing 6 7
Reynaga Celina Conceptualization Formal analysis Writing – review & editing 1
https://orcid.org/0000-0001-5307-7536
Sandoval Salinas María Leonor Data curation Writing – review & editing 13 14
Chillo Verónica Data curation Investigation Writing – review & editing 15
https://orcid.org/0000-0001-8181-5049
Piquer-Rodríguez María Conceptualization Formal analysis Funding acquisition Investigation Methodology Project administration Supervision Writing – original draft Writing – review & editing 16 *
1 Instituto de Biodiversidad Neotropical (IBN), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Facultad de Ciencias Naturales e Instituto Miguel Lillo, Universidad Nacional de Tucumán (UNT), Yerba Buena, Tucumán, Argentina
2 Instituto de Investigaciones Territoriales y Tecnológicas para la Producción del Hábitat UNT-CONICET, Tucumán, Argentina
3 Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA), CONICET, Universidad Nacional de Cuyo (UNCuyo), Mendoza, Argentina
4 Instituto Vertebrados, Zoología, Fundación Miguel Lillo, Facultad de Ciencias Naturales e Instituto Miguel Lillo, Universidad Nacional de Tucumán, Yerba Buena, Argentina
5 Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT)- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Yerba Buena, Tucumán, Argentina
6 Instituto Argentino de Investigaciones de las Zonas Áridas (IADIZA), CCT-CONICET, Argentina
7 Facultad de Ciencias Agrarias, Universidad Nacional de Cuyo, Mendoza, Argentina
8 Fundación Miguel Lillo, Tucumán, Argentina
9 Universidad Nacional de Tucumán, Argentina (UNT), Argentina
10 Universidad Nacional de Santiago del Estero, Argentina (UNSE), Argentina
11 Centro de Referencia para Lactobacilos CCT NoA Sur. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
12 Instituto Superior de Investigaciones Biológicas (INSIBIO), CCT NoA Sur. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
13 Instituto de Investigación en Luz, Ambiente y Visión (ILAV), CONICET-UNT, Argentina
14 Instituto de Investigaciones en Biodiversidad Argentina (PIDBA), Universidad Nacional de Tucumán (UNT), Yerba Buena, Tucumán, Argentina
15 Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB) IFAB INTA-CONICET, Agencia de Extensión Rural de El Bolsón, Argentina
16 Institute of Geographical Sciences, Freie Universität Berlin, Berlin, Germany
González Brambila Claudia Noemi Editor
Instituto Tecnologico Autonomo de Mexico, MEXICO
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: maria.piquer-rodriguez@fu-berlin.de
19 9 2024
2024
19 9 e03078134 1 2024
11 7 2024
© 2024 Fontanarrosa et al
2024
Fontanarrosa et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Biographical features like social and economic status, ethnicity, sexuality, care roles, and gender unfairly disadvantage individuals within academia. Authorship patterns should reflect the social dimension behind the publishing process and co-authorship dynamics. To detect potential gender biases in the authorship of papers and examine the extent of women’s contribution in terms of the substantial volume of scientific production in Ecology, we surveyed papers from the top-ranked journal Ecology from 1999 to 2021. We developed a Women’s Contribution Index (WCI) to measure gender-based individual contributions. Considering gender, allocation in the author list, and the total number of authors, the WCI calculates the sum of each woman’s contribution per paper. We compared the WCI with women’s expected contributions in a non-gender-biased scenario. Overall, women account for 30% of authors of Ecology, yet their contribution to papers is higher than expected by chance (i.e., over-contribution). Additionally, by comparing the WCI with an equivalent Men’s Contribution Index, we found that women consistently have higher contributions compared to men. We also observed a temporal trend of increasing women’s authorship and mixed-gender papers. This suggests some progress in addressing gender bias in the field of ecology. However, we emphasize the need for a better understanding of the pattern of over-contribution, which may partially stem from the phenomenon of over-compensation. In this context, women might need to outperform men to be perceived and evaluated as equals. The WCI provides a valuable tool for quantifying individual contributions and understanding gender biases in academic publishing. Moreover, the index could be customized to suit the specific question of interest. It serves to uncover a previously non-quantified type of bias (over-contribution) that, we argue, is the response to the inequitable structure of the scientific system, leading to differences in the roles of individuals within a scientific publishing team.

http://dx.doi.org/10.13039/501100021778 Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación PICT 2019-4546 https://orcid.org/0000-0001-5842-2263
Fontanarrosa Gabriela http://dx.doi.org/10.13039/501100021778 Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación PICTO Género 0022-2022 Nuñez Montellano María Gabriela This research received funding from the Freie Universität Berlin (https://www.fu-berlin.de/) awarded to MPR; and Agencia (http://www.agencia.mincyt.gob.ar/): PICT 2019-4546 awarded to GF and PICTO Género 0022-2022 awarded to GNM. Sponsors or funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data AvailabilityRaw data is available in Fontanarrosa, G.; Zarbá, L.; Aschero,V; Dos Santos, DA; Nuñez Montellano, M.G.; Plaza Behr, M.,; Schroeder, N.; Lomáscolo, S.; Fanjul, M.E; Monmany Garzia, C.; Alvarez, M.; Novillo, A.,; Lorenzo Pisarello., M.J, D’Almeida, R.E.;, Valoy, M.; Ramírez-Mejía, A.F; Rodríguez, D.; Reynaga, C.; Sandoval Salinas, M.L.; Chillo, V. & Piquer-Rodríguez. Ecology Authorships Gender 1999-2021. Fighare. 2024. 10.6084/m9.figshare.25953058.
Data Availability

Raw data is available in Fontanarrosa, G.; Zarbá, L.; Aschero,V; Dos Santos, DA; Nuñez Montellano, M.G.; Plaza Behr, M.,; Schroeder, N.; Lomáscolo, S.; Fanjul, M.E; Monmany Garzia, C.; Alvarez, M.; Novillo, A.,; Lorenzo Pisarello., M.J, D’Almeida, R.E.;, Valoy, M.; Ramírez-Mejía, A.F; Rodríguez, D.; Reynaga, C.; Sandoval Salinas, M.L.; Chillo, V. & Piquer-Rodríguez. Ecology Authorships Gender 1999-2021. Fighare. 2024. 10.6084/m9.figshare.25953058.
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pmcIntroduction

Meritocracy is a theoretical social system of personal advancement, promotion, and recognition, depending exclusively on a combination of individual attributes: training, talent, and effort, i.e., merit [1, 2]. The concept of meritocracy emerged as a contrast to aristocracy, suggesting that a person’s position in society should be based on their achieved merits rather than their inherited familial status [1]. The scientific system explicitly aims to be meritocratic, objective, and neutral, applying mechanisms and policies to guarantee this, such as thorough evaluation by peers of demonstrated achievements and capacities [3]. However, the assumptions of meritocracy in terms of equal opportunities and fair competition [2, 4, 5], are not fulfilled in science due to the proven existence of inequalities that interfere with the chances of goal achievement [6–8]. Moreover, the notion of meritocracy disregards the historical and political aspects of individuals’ circumstances and therefore may justify inequalities [1].

Inequalities in science negatively affect people based on their identity and biographic features such as social and economic status, ethnicity, sexuality, care tasks, and gender, among others, and the intersectionalities among those attributes [1, 7, 9]. In particular, gender bias results from multiple interactions and feedback loops that occur across various scales, ranging from individual and family levels to workplaces and societal structures [10]. In academia, the existence of gender bias is particularly well-supported by a growing number of studies documenting differential barriers for women across the globe [10–12] affecting well-being perception, productivity (i.e., number of papers published) [13, 14], academic impact (i.e., number of citations), career length [14], research team constitution [15–17], and peer recognition [2, 8, 18–21], among others.

Gender bias in academia can be classified into two main types: i) obstacle bias, and ii) requirement bias. Obstacle bias refer to the barriers that women face in their academic careers, such as double burden (i.e., academic work and domestic unpaid work), less intellectual stimulation, less support, fewer role models, and sexual harassment, among others [10, 17, 21, 22]. Requirement bias refers to the commonly implicit higher expectations placed on women’s work, recommendations, and hiring evaluations compared to men’s, perpetuated by both men and women. In other words, for equal merits, men are better rewarded than women [2, 8, 19, 20] but see [23]. The biases in requirements and the undervaluation of women in comparison to men, despite having equal merits, are two interrelated aspects of gender bias. The requirement bias translates into an undervaluation of female researchers’ contributions within research teams [3]. For women’s contributions to be perceived and evaluated as equivalent to men’s, it has been suggested that female researchers must outperform them in terms of the amount and quality of papers [2, 19, 20].

Authorship is central to the recognition and reward system within the historically expanding and evolving network of ideas, papers, and scholars´ contributions [24–26] that shapes scientific knowledge. It directly influences researchers’ career prospects [27–30] and plays a crucial role in the scientific community. The order of authors in a co-authorship list typically reflects their degree of contribution in terms of time investment to a paper, with the first author contributing the most, and the contribution decreasing with each subsequent position. The last author may or not reflect an advisory role [8, 28, 30]. Nevertheless, a co-authorship position may also imply other or even arbitrary decisions (but see [31]). Despite that, a bibliometric analysis of the authors’ inclusion in a paper and their positions may capture key aspects of the social dimension behind the publishing process and co-authorship dynamics [32, 33]. In this study, we present a gender-based bibliometric analysis of authors’ contributions to the papers published in the journal Ecology (henceforth Ecology) between 1999 and 2021 as a study case of a high-ranking journal in ecology, a Science, Technology, Engineering, and Mathematics (STEM) discipline, where gender bias is reported [14]. Our analysis investigates potential gender biases in the authorship of papers and examines the extent of women’s contribution to the volume of scientific production in the ecology discipline.

Methods

Study case: The field of ecology and the journal Ecology

Ecology is a field of study within biology that focuses on the relationships between living organisms and their physical environment. Ecological studies also provide information about nature’s contributions to people and how we can use the Earth’s components in a way that maintains a healthy environment for future generations [34]. Within STEM, men’s careers in biology are, on average, 19% longer than women’s, resulting in a gender bias of total productivity that exceeds 35% [14]. This gender bias in career length is greater in biology than in applied physics, for example [14]. The ecological literature is dominated by male scientists mainly from North America and Europe [9, 29]. The journal Ecology is edited by the Ecological Society of America (ESA) and published by Wiley-Blackwell editorial. It was established in the United States in 1920 and has a high impact factor (4.8 for 2022) within the field. Their papers greatly contribute to shaping the global ecological agenda and conceptual framework. The decision to analyze data from the field of ecology was based on several factors: (1) most of the authors posing the research question belong to the discipline of ecology; (2) ecology serves as a representative STEM discipline where men outnumber women in authorship. In many fields, including ecology, women constitute about 30% of all authors [29, 35]; (3) the common convention in ecology, as in other STEM fields, is to assign the first author position to the individual who contributed the most to the study [30]. Therefore, authorship bias in ecology could be indicative of patterns in other STEM disciplines that exhibit similar trends, particularly biology, chemistry, and mathematics, which have been identified as more gender-balanced than other fields [36]. Thus, the community of researchers publishing in Ecology provides a suitable model for investigating hierarchical gender bias in science.

Data acquisition, data curation, and limitations

We surveyed all Ecology papers in the categories “Papers”, “Reports”, “Reviews” and “Special Issues” from 1999 to 2021 (22 years). For each paper we recorded the list of authors and classified them as “man” or “woman” using the first name as a proxy of gender by checking available databases (such as Gender Checker, 2020, available at: https://genderchecker.com/), and when necessary, by searching for the authors in Google Scholar or their ResearchGate profiles, among other academic social networks [16, 21]. This strategy is more accurate than automatized classifications [37]. We excluded any paper in which the gender of an author could not be identified. Out of 6125 articles in ecology in the surveyed categories, 993 articles were discarded because they contained at least one author whose sex could not be reliably determined, leaving 5132 articles in the dataset we used for the analyses.

We acknowledge that our approach has limitations. Firstly, it is limited by binarism and cannot fully capture the self-perceived gender of authors. Additionally, there may be a bias because the public databases we used do not fully represent a diverse range of nationalities and cultural backgrounds. Nonetheless, we do not anticipate substantial changes to our main results based on a previous report that highlights the dominance of authors in top-ranked ecology journals from the United States, the United Kingdom, Australia, Germany, and Canada, which account for over 75% of the top-publishing authors [9]. Meanwhile, other regions from the Global South, as well as Russia, Japan, and South Korea, are strikingly underrepresented in top-ranked ecology journals [9].

Data analysis

Gender data overview

We employed a battery of descriptive statistics for a general exploration of data structure, focusing on the distribution of genders in the paper’s authorships. We explored multiple dimensions of overall data structure, by year and by paper, including the number of authors, authorship, participation, and temporal trends of those variables by gender. Authors account for every person that appeared at least once in our data set. Authorship accounts for the number of authoring events disregarding the author’s identity, therefore, a particular author could account for more than one authorship event, and the number of total authorships is higher than the number of total authors. Publication instances represent the number of events in which the same author has participated in different papers. We have classified paper types considering their gender composition and author numbers and calculated their frequencies.

Women’s contribution index

To estimate the relative contributions made by female authors to a given paper, we designed the Women’s Contribution Index (WCI). The WCI is constructed based on the Harmonic Allocation of Authorship Credit following Hagen [38] (Eq 1). The harmonic counting allocates credits according to authorship position in the author list and the number of co-authors. Here we consider that author credit is a proxy of the contribution in terms of time investment of the ith author in a particular paper sensu [38]. The assumption under this approach is that the total publication credit is shared among all co-authors, the first author gets the most credit, and in general, the ith author receives more credit than the (i+1)th author. The greater the number of authors per paper, the less credit per author. For the sum of every author’s contributions in the paper to be 1 (i.e., to be normalized), each reciprocal author position (i.e., 1/author position) is divided by the summation of all reciprocal positions (i.e., for a three-author paper: 1/1 + ½ + 1/3).

The harmonic credit for the ith author, Ci (i referring to the position along the authors’ list) in a particular paper with N co-authors (following [38]), is calculated as follows: Ci=1i/[1+(12)+(13)+(14)+…(1N)] (1)

The WCI accounts for the sum of the author´s contribution (Ci) of every woman within an author’s list (Eq 2). The WCI takes values between 0 (no women contribution) to 1 (complete women contribution). Fig 1 shows an example.

10.1371/journal.pone.0307813.g001 Fig 1 Graphical representation of the logic behind the Women’s Contribution Index (WCI) based on the binary author list in papers following Hagen [38].

A. Encoding of the binary list for a five-authored fictitious paper example. B. Author contribution by positions following the harmonic allocation of authorship credit sensu Hagen [38]. C. Women’s Contribution Index the calculation for the exemplar paper in B. The index is the sum of the contributions of each woman in the papers. Each woman’s contribution is dependent on her position and the whole number of authors. The pie chart depicts a paper of five authors following the exemplar author list in B.

WCI=∑i=1NCi*Gi (2)

Where:

Ci: is the ith author credit sensu harmonic allocation [38]

Gi: is the gender binary codification of the author of the i position. 1 = woman; 0 = man.

N = is the number of authors in a particular paper

Women’s contribution index in an unbiased scenario

To test whether women’s contributions align with what is expected by chance, we compared the sum of the WCI of the entire dataset (observed total WCI) with its equivalent value in gender-unbiased scenarios (simulated total WCI). We ran 10,000 simulated scenarios where, for each article, we randomly rearranged authors’ positions while maintaining the article’s gender ratio. We calculated the total WCI for each simulation and obtained a distribution of total WCI values, with the mean representing the simulated total WCI. We tested the following statistical hypothesis:

H0 = There is no gender contribution bias within the whole dataset of papers on Ecology 1999–2021.

H1 = There is a gender contribution bias within the whole dataset of papers on Ecology 1999–2021.

Additionally, we computed the theoretical expected WCI value (expected total WCI) calculated as the sum of the proportions of female authors by article. The expected theoretical expected WCI value depends directly on the women’s proportion within the author lists. In a non-biased scenario, the women’s contribution index calculated should fit with the expected one.

Women’s contribution index vs. men’s contributions index

To improve the robustness of our analysis, we compared the WCI to the MCI (Men’s Contribution Index) in mixed-gender papers. To avoid dependency on the data we randomly divided the data set into two subsets of equal size. For one subset we calculated the WCI per paper and for the other subset, we calculated the MCI per paper. The MCI followed a procedure identical to that of the WCI, but accounting only for men’s contributions. For comparability purposes, we centered the WCI and MCI through the procedure of subtracting their respective expected values (i.e., women’s and men’s proportions by paper), and we obtained the centered WCI and the centered MCI. Centering is crucial for interpretation when we are interested in group effects [39].

To compare centered WCI vs. centered MCI we performed a quantile-quantile plot (q-q plot). A q-q plot is a plot of the quantiles of the first dataset against the quantiles of the second dataset and is used for diagnosing if two data sets come from populations with a common distribution [40]. Additionally, we performed a Kolmogorov–Smirnov statistical test that quantifies a distance (i.e., dissimilarity) between the empirical distribution functions of two samples (the centered WCI and centered MCI, in our case) [41]. The null distribution of this statistic is calculated under the null hypothesis that the samples are drawn from the same distribution (in the two-sample case).

All the analyses were performed in the R environment (R version 3.6.1 [42]) using the base and tidyverse packages [43]. To preserve the identity of the authors, the database has been encrypted. The encrypted data and [44] executable R code [45] behind all the analysis are available in a permanent repository. Additionally, a printed version of the codes and results are available as Supplementary Information (S1 File). Figures were edited using Inkscape (https://inkscape.org/). To maintain consistency and help readers easily interpret the graphs, we followed the color coding of Grosso et al. [16], purple (RGB:542583ff) for women, yellow (RGB:fcb827ff) for men, and red (RGB:ff2b2aff) for mixed conditions.

Results

Gender data overview

A total number of 5,132 papers were analyzed between 1999–2021. Of 11,236 authors in those papers, 3,589 (31.94%) were coded as women. Some authors participated in more than one paper and thus, there were 18,237 authorships, from which 5,074 (27.82%) were coded as women’s authorships (Fig 2A). The average publication instances per author was 1.62 (1.41 women, 1.72 men). The average number of authors per paper was 3.55. The four most common paper types consisted of two male-authored papers; 3 mixed-authored papers; 4 mixed-authored papers, and two mixed-authored papers. A list of these and other key numbers are displayed in Table 1.

10.1371/journal.pone.0307813.g002 Fig 2 Gender Data Overview: A. Pie chart of gender proportion among total authors (outer circle); women proportion among total authorships (middle circle), and women proportion among the 50 authors who published the most (inner circle). B. Absolute frequency histogram of the author’s position in the author list discriminated by gender for the entire study period. C. Absolute frequency histogram with the number of papers published by authors during the whole period. The chart shows the top 100 authors who published the most (same author in more than one paper) in decreasing order. The percentage of women among the first, 100, 50, and 25 top authors are shown on the right. D. Upper figure: yearly absolute number of papers published and classified into three categories: exclusively female author lists, exclusively male author lists, and mixed author lists. Middle chart: yearly total authorships in Ecology. Lower chart: yearly women’s proportion within the authorships.

10.1371/journal.pone.0307813.t001 Table 1 Key summary values.

Key summary variables	Value	
The average number of authors per paper	3.55	
The average republication value	1.62	
The average republication value of women	1.41	
The average republication value of men	1.72	
Women proportion among the 100 top-publishing authors	0.15	
Women proportion among the 50 top-publishing authors	0.14	
Women proportion among the 25 top-publishing authors	0.04	
Observed total Women’s Contribution Index	1456.08	
Expected total Women’s Contribution Index (based on female authors per paper)	1360.49	
Mean simulated total Women’s Contribution Index	1360.62	
The standard deviation of the simulated total Women’s Contribution Index	7.75	
Max simulated total Women’s Contribution Index	1388.23	
The most common authorship length in female mono-gender articles	2	
The most common authorship length in male mono-gender articles	2	
Maximum authorship length in female mono-gender articles	5	
Maximum authorship length in male mono-gender articles	13	
Absolute frequency of most common authorship structure: 2 male	842	
Absolute frequency of 2nd most common authorship structure: 3 mixed	686	
Absolute frequency of 3rd most common authorship structure: 4 mixed	531	
Absolute frequency of 2nd most common authorship structure: 2 mixed	480	

The table displays the most relevant values extracted from the descriptive statistics and analyses conducted throughout the paper.

Regarding gender position trends in the authors’ lists, the most frequent position of female authors was the first one; this frequency monotonically decreased towards backward positions (Fig 2B). Among male authors, both first and second positions shared the higher frequencies in the authors lists, and from the third position backward, frequencies decreased monotonically (Fig 2B).

We observed a strong decrease in the presence of female authors among the authors who published the most (data subsets of 100, 50, and 25 authors were considered). Among the top 100, 50, and 25 authors, 15%, 14%, and 4% were women, respectively (Fig 2C).

Overall, the number of papers authored exclusively by women is very low throughout the studied period. The most notable trend observed is an increase in the number of mixed-gender papers and a decrease in papers exclusively authored by men, with a trend toward reduction (Fig 2D). The maximum authorship length in female mono-gender articles was five, while the maximum authorship length in male mono-gender 13 (Table 1).

Overall, there was an incremental trend in the proportion of women authors, particularly noticeable from 2005 onwards (Fig 2D). Starting around 2012, the ratio of women to total authors appears to stabilize around 0.3 to 0.35. Likewise, the average number of authors per year increased systematically in the surveyed period (Fig 2D).

Women’s contribution index (WCI): Women’s roles in the publishing dynamics

We measured the WCI for each paper in our dataset and found that the total sum value for all papers was 1,456. Upon conducting the randomized simulations, we obtained a simulated WCI distribution with a mean value of 1,361 and a standard deviation of 7.75. Notably, even the maximum value in the WCI distribution after running the 10,000 randomized simulations (1,388) did not exceed the observed value of WCI (vertical violet line in Fig 3A, Table 1). Therefore, we could confidently reject the null hypothesis that pointed to no bias in the WCI across the entire volume of Ecology papers from 1999 to 2021, for the 10,000 simulated scenarios. None of the simulated scores surpass the observed WCI (P < 1e-4). Consequently, the cumulative contributions of women exceeded the expected value by chance.

10.1371/journal.pone.0307813.g003 Fig 3 Women over-contribution to papers.

A. Frequency distribution of the women’s contribution index (WCI) in 10,000 random scenarios. The mean value of this distribution is depicted by a thick black vertical line and its standard deviation by dotted vertical lines. The purple vertical line indicates the observed WCI. B. Quantile-quantile plot comparing the cMCI (X-axis) vs the cWCI (Y-axis). The plot displays the pairs of quantiles for probability order quantiles from 0 to 1 (p-order quantiles). p-order quantiles are shown by the colored gradient (blue = 0; red = 1). A 45-degree reference line is also plotted (dotted line) as a reference.

This observation gained further support when comparing the centered WCI with the centered MCI along the entire rank of p-order quantiles (Fig 3B). The trend demonstrated that the centered WCI consistently exceeded the centered MCI, as indicated by the data points above the reference diagonal line representing women’s contributions (Fig 3B). If both data sets came from a population with identical distribution, the points should align along the diagonal reference line. The greater the deviation from this reference line, the stronger the evidence supporting that the two data sets came from different statistical populations. Furthermore, using a Kolmogorov-Smirnov test, we found that the compared distributions were significantly different (D = 0.13971, p-value < 2.2e-16). Consequently, we can confidently reject the null hypothesis that the centered WCI and the centered MCI distributions were derived from the same population.

Discussion

Through the analysis of author lists, we quantified the overall contribution by women in generating the volume of publications of the journal Ecology for over 20 years. Our bibliometric analysis demonstrated that, since 1999, papers in Ecology have been mainly men-dominated. Beyond the underrepresentation of women publishing in Ecology, our main finding indicated that their contribution, considering the author’s position, exceeded what would have been expected by chance. This means that the few women publishing in Ecology tend to occupy positions in the author list that require a greater time investment. We referred to this pattern as "over-contribution".

Measuring gender inequities

To measure gender bias, several indicators have been defined and implemented at different scales [10, 14]. Various studies assessed gender bias in terms of gender disparities in authorship, number of published papers, citations, or access to funding in almost all disciplines and countries worldwide [11, 13, 32, 46]. However, gender bias is a multidimensional problem rooted in a historical gender imbalance that impacts the success rate of women in academia, therefore, no single indicator is capable of including all its relevant dimensions [10, 20]. For example, women’s ratios in the workforce represent the most common tool for diagnosing gender bias, but despite it being a valuable tool it can mask some important dimensions of inequalities such as team members’ roles [16, 21, 47]. Grosso et al. [16] used graph theory and found that regardless of the near parity of women representation among Argentinean and Brazilian herpetologists, women were marginalized within their co-author networks, due to the generalized preference of male authors to collaborate with other male authors (i.e., male homophily). Our findings indicate a pattern of male homophily, evidenced by the maximum author list length for exclusively male-authored papers being 13, compared to 5 for exclusively female-authored papers. This trend is further reflected in the frequency ranking of paper types, where the most common format is a paper authored by two men. In contrast, the equivalent female-only paper, authored by two women, ranks fourth and represents half the number of the two male-author papers. Additionally, papers authored by a single male occupy the sixth position in the ranking, whereas those authored by a single female are placed in the twelfth position. This aligns with the results of Fox et al. [23], who found that women are significantly underrepresented as sole authors compared to their representation in multi-authored papers.

Beyond these general patterns of data and homophily, our work is notable for considering the gender and positions of all co-authors. Our methodological proposal for measuring gender inequities deepens the approach by examining the roles of team members and quantifying authors’ contributions to scientific papers by their harmonic weights [38]. Using harmonic weights corrects for inflationary and equalizing biases that can arise when authorship credit is allocated either by issuing full publication credit repeatedly to all coauthors, or by dividing one credit equally among all co-authors [38].

In our work, we considered that the last author had the lowest contribution along the author’s list, which may be controversial due to the last author not always playing the same role [48] (see discussion in S2 File). We have categorized the potential errors based on how we assess the last author’s contribution: either assuming the last author contributed more than the preceding authors (Type A) or assuming they contributed the least (Type B). Type A error occurs if we underestimate the last author’s contribution, considering it poor when, in fact, they might be: A.i: A senior author who has contributed at least more than the preceding author on the list [30]. Type B error occurs if we overestimate the last author’s contribution, considering it significant when they might be: B.i: A gifted author; B.ii: A guest author; B.iii: The one who contributed the least; B.iv: Someone positioned last due to their surname’s initial letter being later in the alphabet than the preceding authors’; B.v: An author randomly positioned last. Given these scenarios, the most error-avoidant decision is to consider the last authors as having the least contribution [48, 49]. By not assuming that the last author is a senior author, we risk the opposite error: undervaluing their actual contribution. In those cases in which the last author acts as a group leader, she/he may be contributing to many works in parallel and thus their time investment must be distributed. This also supports the idea that the WCI can effectively measure the time invested in papers, as it considers both the number of authors in a paper and their position in the author list. Some research teams determine the order of author positions in an alphabetical listing based on the initial of the last name [50]. This particular practice is not of concern to our study, as in the event of numerous papers adopting this approach, the calculated WCI value would tend to resemble the expected chance value. Therefore, if it has any effect at all, it would likely lead to an underestimation of our result of women’s contribution. Based on the aforementioned, the WCI provides a valuable tool for quantifying individual contributions and understanding gender biases in academic publishing. We believe that our methodological proposal represents a reasonable new way of measuring gender bias able to capture broader information than previous methodological alternatives [10, 16, 50]. Moreover, the index could be customized to suit the specific question of interest or different assumptions of author inclusion and allocation based on additional information [38], for further arguments on how to value the author’s contribution see the S2 File.

Temporal trends within authorships

Our results showed that there was a notable temporal trend over the last 22 years, indicating a consistent rise in both the total number of authorships (from around 500 to 1000 per year) and women’s authorships percentage in Ecology papers (from 25% to 35% of overall authorships). This aligns with the current trends observed in another ecological journal [29]. In addition, we found that mixed-gender papers increased during the study period, while papers written exclusively by men or women exhibited a declining trend over time. These patterns are in tune with the emergence and strengthening of modern scientific patterns, such as big science and technoscience [51], which are characterized by the exponential growth of multi-authored publications and larger team sizes [26, 52–54].

Hierarchical organization of gender bias

We recognized three levels of gender bias that added evidence to the scaling pattern of gender bias in the global workforce [55]. The first level of bias we registered was the overall low proportion of women (i.e 30% of authors) publishing in Ecology during the period we analyzed. This magnitude matches the general trend of women participation already reported in other biological fields in several academic postgraduate communities from high-income countries [11, 13, 14, 50, 56, 57]. While it may be difficult to accurately estimate the global number of female ecologists in the academic realm, women represent 53% of bachelor’s graduates, 43% of Ph.D. graduates, and only 28% of researchers in the field of ecology worldwide [58]. The second level of bias we found was that the female authorship percentage (accounting for the number of authoring events that disregard the author’s identity) was lower than the female authors’ percentages. We found a third level of bias when we analyzed the proportion of women among the authors who published most frequently in Ecology. The dearth of women becomes more pronounced in progressively more restrictive subsets of authors, who published more times in Ecology (i.e., top 100-50-25 authors). This represents a third bias level, with only 15% of women among the top 100 publishing authors between 1999 and 2021. Given that other authors reported up to 4% of women among the 100 top-publishing authors in Ecology in the 1945–2019 period [9], our results suggest an increase in women’s representation among top publishing authors in the last 20 years. It is still unclear whether the frequency of women publishing in Ecology matches the rate of women making it to the list of top 100 authors.

The gender bias of the authors and authorship proportion and their decrease in top-publishing authors in Ecology shown in our work seems to be compatible with the hardening of academic demands: the higher the demands, the higher the gender bias [59]. A recent study by Andersson et al. [2] found that what we call requirement bias (i.e., the often implicit, higher demand in women’s performance), increases as productivity increases. Also, it has been suggested that as researchers progress in their academic careers, requirement bias (unfavorable towards women) intensifies [55]. These increasingly greater difficulties in career advancement promote two main effects: 1- the impediment of women’s advancement at the same rate of recruitment (for example when comparing Ph.D. and Senior researchers) [21] and commonly referred to by the metaphor “glass ceiling” [55], and 2- the higher propensity of women to leave academia after their Ph.D. compared with their male colleagues [3] and commonly referred to by the metaphor “Leaky pipeline” [60, 61]. Both effects would point to the hierarchical organization of gender bias found in our data. In addition to these complex phenomena, we hypothesize another phenomenon to take into account: over-compensation.

From over-contribution patterns to the over-compensation hypothesis

Our results from the WCI (Women’s Contribution Index) showed a strong pattern of women’s over-contribution in scientific publishing. This does not necessarily involve glorifying it as accomplishments of women who published in Ecology, despite their significant efforts. If doing, so we would be making a frequent statistical mistake known as survivorship bias [62, 63]. Survivorship bias, a form of selection bias, is a logical fallacy that involves focusing on the people or things that passed a selection process while overlooking those that did not, typically because they lack visibility. Survivorship bias leads to a more optimistic interpretation than the data offers and can lead to false conclusions in many different ways.

The over-contribution of women we observed could lead to a different interpretation of acknowledging it as an accomplishment. Instead of solely considering those "survivor" women who managed to publish in prestigious journals like Ecology, we can broaden the analysis to acknowledge that some women did not overcome the gender barriers. In this context, we could interpret the over-contribution pattern as arising from an attitudinal and psychological mechanism known as "over-compensation". Over-compensation was a term first proposed by psychologist Alfred Adler [64], that in the context of our study was coined merely conceptually. It involves the conscious or subconscious mechanism of concealing real or imaginary weaknesses, frustrations, inadequacies, or incompetence in one area of life by achieving excellence in another area. Thus, it is tightly associated with self-perception [64]. The higher proportion of women’s contributions compared to what would be expected by chance suggests an adaptive strategy employed by female authors seeking to publish in Ecology papers.

Previously it has been suggested that women may over-compensate due to gender bias in the workplace [2, 20]. To overcome stereotypes and prove their competence, women may feel pressure to work harder, be more competent, and demonstrate their skills more strongly than men do. This phenomenon is colloquially referred to as the "prove-it-again", where women are evaluated more harshly and held to higher standards than men (i.e., requirement bias) [59]. In conjunction with the generally lower self-perception of women’s abilities [17], the lower peer valuation of women can also trigger the phenomenon of over-compensation. In their seminal study, Moss-Racusin et al. [19] demonstrate that female candidates with equivalent academic qualifications for a technician position were perceived as less competent and less suitable for hire compared to male candidates. This suggests that a female scientist must surpass a male counterpart in performance to be considered comparable [20]. Moreover, Ross et al. [8] have recently studied the necessary level of work required for members of a research team to become an author, highlighting that it is more difficult for women than for men to be invited as co-authors. Thus, women must compensate for this bias with significantly more effort for their scientific contributions to be recognized. Thus, the female authors of Ecology may have had to exert more effort and invest more time in research than the average to become part of the research team behind a paper, as our WCI shows. The results of our work are largely compatible with the overcompensation hypothesis we are proposing. Future studies designed for this purpose will likely shed light on this potential phenomenon.

The women´s over-contribution pattern we found is prone to be a consequence of the higher dropout rates of women. Women in STEM fields have higher dropout rates than their male counterparts [14, 65, 66]. Unfortunately, information regarding other biographic features of Ecology authors, such as their career stages, or the length of their academic trajectories was not available in our analyses. Thus, the bias we found towards women occupying the first author could be explained, at least partially, as a side effect of the presumably shorter careers of women authors of Ecology, which are dropped out by scientific pressures after publishing their first publication [14]. The rationale behind this involves that, early career researchers often occupy first author positions while seniors tend to occupy the last position (this is not always clear as discussed above).

However, there is a more complex scenario to consider, in which the dropout serves as both the cause and consequence of the observed differential contribution. One aspect of this differential gender contribution can be explained by the lower representation of women in advanced career stages compared to early stages, resulting from the dropout effect [14]. Related to the development of the career stage Manlove & Belou [67] described the proportion of lead authors, editorial board members, and editors of published manuscripts and found that lead authors with female names represent a large proportion of lead authors compared with editor positions. Additionally, the differential women’s dropout effect itself could be attributed to over-compensation. This notion is based on the understanding that over-compensating requires a significant effort that becomes challenging to sustain over time, particularly for individuals facing not only inequalities related to career requirements (i.e. requirement bias) but also gender biases in other aspects (such as care tasks (i.e. obstacle bias) [21].

Future studies that consider both patterns of positions in the author list together with career trajectories could provide insights into the extent to which over-compensation influences gender-biased decisions to leave science (i.e., dropout).

Final considerations

Our study’s findings regarding the underrepresentation of female authors and their disproportionate high contribution to the Ecology Journal challenge the notion of science as an objective entity unaffected by biographic features, like gender. The idealized concept of science being objective often overlooks the historical context where science’s practice and transmission were predominantly the domain of men, resulting in an inherently biased system [68–71]. Science’s traditional foundations were shaped by and for white men, built upon ideals of objectivity, neutrality, and universality [69]. In this context, a theoretically meritocratic system may appear to align with the principles of this male-dominated scientific model. However, our findings support that the assumptions behind meritocracy do not hold and that survivor bias is responsible for masking the differential barriers to which people belonging to marginalized groups in academia are subjected.

Our results suggest that requirement bias may compel women in science to overcompensate, influencing their decisions to either persevere or exit the field. This possibility warrants further investigation through targeted experimental designs. Our research challenges the notion of meritocracy, allowing for a more comprehensive examination of the system compared to conventional approaches that overlook the experiences of marginalized women.

Supporting information

S1 File Codes for women over-contribution analyses.

Printed version of the R markdown code for the data analysis and complete results in html format.

(HTML)

S2 File Methodological considerations for quantifying author contributions.

In this appendix, we present the rationale behind our methodological decision to consider the last author as the one who contributes the least. We believe this decision minimizes the likelihood of error in quantifying the last author contribution. Additionally, we propose potential methodological alternatives that the Women Contribution Index allows for when deemed appropriate by the researcher.

(PDF)

S3 File Alternative language abstract (Spanish).

(PDF)

We thank Jimena Grosso et al. [16] for providing us with the template for data acquisition; members of CienciaFem and other volunteers who helped in data collection; Jéssica Frattani helped us with the code; Instituto de Biodiversidad Neotropical (IBN) provided us a co-working space for the required meetings. We acknowledge the Postgraduate Secretary of the Faculty of Natural Sciences & Instituto Miguel Lillo, Universidad Nacional de Tucumán, for providing us with the physical space and institutional endorsement for the postgraduate course from which a large part of the results of this work derives. We also thank Claudia Noemi González Brambila, Joanna M Setchell, and one anonymous reviewer for helpful comments on the manuscript. We would like to express our gratitude to the Argentine public education and scientific systems and their policies aimed at strengthening science and gender equality in science. Additionally, we wish to voice our concern regarding the recent wave of governmental decisions negatively impacting Argentina’s scientific system and infrastructure.

10.1371/journal.pone.0307813.r001
Decision Letter 0
González Brambila Claudia Noemi Academic Editor
© 2024 Claudia Noemi González Brambila
2024
Claudia Noemi González Brambila
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
4 Apr 2024

PONE-D-24-00453Over twenty years of publications in Ecology: Over-contribution of Women reveals a new dimension of gender biasPLOS ONE

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Reviewer #1: This manuscript investigates an important question - that of gender bias in publications in a STEMM subject - and introduces a new index to test for gender bias in the contribution to publications. The measure can easily to be extended to examine other biases, and is extremely useful. The dataset is impressive and the results are important and shocking, if unsurprising. The authors argue convincingly that they detect over-contribution by women, and link this to the inherently inequitable structure of the system we work in.

My only question, as I read the manuscript, is addressed in the discussion: the implications of the last author position being more regarded as of higher value than most other positions, at least in some cases (https://doi.org/10.1002/ece3.3435). The authors could present data on last author gender, perhaps compared with first author gender, but given that they include their data and code, others can explore these patterns, so this isn't necessary here.

I appreciate the clear statement of the limitations of the methods on p7.

Overall the manuscript is very easy to read. I recommend:

1. Ensuring that the figures are legible in greyscale

2. Checking that the figure captions match the figures, and that the text matches the figures. I could not match the text in LL290-3 to Fig 3, for example. The measured WCI in the text is 1307, but this doesn't match the violet line in Fig 3A. Can you direct the reader to a figure that shows that women's contributions stabilise at 0.3 to 0.35 (L282)?

3. The results of the KS test are missing (L316).

I also have some very minor comments on the clarity of phrasing.

L47: 'academia' (no initial capital)

L52: 'ponder' doesn't seem to be the right word here. Maybe 'measure', 'estimate' or 'quantify'?

L54, L57: 'with' works better than 'vs.'

L58: should Men's contribution index be in italics, to match L51?

L66: I think over-contribution has been explored, but not quantified.

L76: 'this' not 'it'

L87: it's easier to stick with one term than to tell the reader that two terms mean the same thing

L111: no need to refer to the discussion here.

L116: 'published in the journal Ecology' will do here

L117: a Science, Technology, Engineering, and Mathematics (STEM)

L188: 'reported' not 'remarked'

L134: 'within the discipline' or 'within the field' but not both

L145: 'excluded' rather than 'dismissed'

L149: can you use a different word for 'non-occidental'? Later you use 'Global South', which works better.

L166: should 'Publication instances' also be in bold, like other variables?

L189: 'Figure 1 shows an example' is all you need

L205: I think A should be B - please check

L219: cut 'Noticeably'

L231: do you need the full explanation of qq plots? I think you can remove LL231-38.

L245: 'in' not 'under'

L266: no need for 'only'

L296: I was taught that we can never be 100% confident

L309: cut 'Furthermore'

L313: replace 'In the case where' with 'If'

L322: cut 'sex-segregated' because it's not quite accurate

L328: no need for 'and we discuss ...' because this is the discusssion

L344: 'pondered' is not the right word here

L381: replace 'during the analysed time-lapse' with 'during the period we analysed'

L387: 'found' not 'registered'

L388: is 'decayed' correct?

L389: I didn't understand this line.

L391: 'The dearth of women'

L392: no need for the italics

L397-8: can you clarify?

L412: there's a problem in the pdf I received here, with an orphan phrase

L431: maybe 'psychologist Alfred Adler'?

L437: cut 'sometimes' because you have 'may'

L444: 'classical' isn't quite right here

L454: cut 'side'

L458: 'the bias we found' not 'the registered bias'

Reviewer #2: Summary of the research

The research uses descriptive statistics, and a novel index (WCI) to describe gender imbalances in author contribution to the journal Ecology. Following the finding that women are responsible for a higher WCI index than expected by chance, the authors reason that overcompensation (a consequence of gender discrimination), is responsible for the finding over overcontribution. This paper is well-written and the ‘from overcontribution to overcompensation’ theory is well argued. However, there are concerns over the presentation of speculations as conclusive evidence in the ‘final considerations’ section of the paper. A few terms, such as ‘contribution’ and ‘productivity’ could be better defined. The research would benefit from statistical tests (for example, on the change in author compositions over time), and more generally by emphasizing findings from the descriptive statistics which are persuasive for a need to explore gender bias in this field, rather than an overwhelming focus on the WCI which has numerous limitations as an index of contribution.

Major comments

The design of the index to describe gender contributions [L169-L207] is novel and provides an interesting contribution to highlight gender imbalances in the wider field. However, the decision to assume that the last author has the least contribution is controversial. The authors justify this contentious decision [L348], elaborate on the limitations that stem from it, and subsequently acknowledge that the last author tends to represent the senior researcher [L462]. Although meanings of authorship positions vary between disciplines, there is strong evidence that in the field of Ecology, most people view the last author as a senior author. For example: Fox, Ritchey and Paine (2018); Duffy (2017); Weltziin et al. (2006). Furthermore, there is a tendency to perceive the ‘corresponding’ author as having a higher contribution to the research. In its current form, the Women’s Contribution Index does not adequately represent the contribution of the final and/or corresponding author. Attributing the final author a proportionally larger contribution than the preceding authors or taking account of the identity of the corresponding authors, are both possible adjustments that would enhance the validity of the index.

[L290 – L293] The stats comparing observed and expected WCI values in this section of the results are confusing to read. The observed WCI value is stated as a ‘total sum value’, and it is presented adjacent to a ‘mean’ expected value, and then a ‘maximum’ expected value. The conclusions of the paper rest on the finding that the simulated WCI value “did not exceed the observed value of WCI” [L294]. This is currently of great concern as the stated observed value of WCI is 1,307 [L291]. The stated expected WCI value is 1,361, with a maximum of 1,388 [L293]. As currently stated, the observed value is less than the expected value (1,307 < 1,361). Figure 3A suggests an observed value of c1450 from reading the value of the purple vertical line. Is the observed value of the WCI quoted incorrectly in this paragraph?

[L480-L495] The paper is mostly consistent in presenting ‘overcompensation’ as a theory to explain the finding of women’s overcontribution. However in the final considerations section, overcompensation is presented as a cause of overcontribution as though there is conclusive evidence for this theory. This does not follow coherently from the body of the paper. Without presenting ‘overcompensation causing overcontribution’ as a theory that remains to be tested, this paper runs the risk of appearing to satisfy a confirmation bias. The abstract sets out an intention to ‘detect potential gender biases in the authorship of papers and examine the extent of women’s contribution to… scientific production in Ecology’ [L48-L50]. The alternative hypothesis [L217] that the observed WCI value differs from the expected WCI value can be met by either woman’s contribution (i) being lower than expected, or (ii) higher than expected. The descriptive statistics very clearly show women are underrepresented: there are fewer female authors (L255: 31.94%), women account for fewer authorship events (L256: 27.78%), and women have lower average publication instances (L257: 1.41 versus 1.72 for men). If the WCI value was observed to be lower than expected, presumably this would have been interpreted as an example of underrepresentation stemming from discrimination. Rejecting the null hypothesis of no difference in observed versus expected WCI values and arriving at the same conclusion (that women are experiencing discrimination in this field), is potentially concerning. The argument of survivorship bias, and the theory of overcompensation causing overcontribution is convincing, but it is strongly recommended that this theory is presented consistently as a theory, not as a conclusion. The authors make no other suggestions to explain women’s overcontribution, which seems amiss.

In the current state of the paper the quoted observed WCI value is less than the expected value, and the WCI suffers from a potential de-valuing of the last (senior) authorship position. The descriptive statistics alone are persuasive that there is a need to address gender bias in this field. For example, the section on hierarchical organisation of gender bias in the discussion [L378-L399] is very well-written and is very persuasive. The paper could benefit from emphasizing known entities (descriptive stats) rather than speculating on more exploratory stats such as the WCI value.

As the authors acknowledge in the discussion [L457], this research would benefit from including other biographic features. The inclusion of career stages and length of academic trajectories could greatly influence the number of authorship events per author, and position in an author list. I look forward to future efforts to explore this question with this in mind.

Minor comments

The paper repeatedly refers to ‘productivity’ but has not defined productivity. For example, [L128]: ‘a gender inequity of total productivity that exceeds 35%’, [L332] ‘gender disparities in … productivity’, and [L402] ‘requirement bias… increases as productivity increase’. Productivity can mean many things and is not sufficiently defined.

[L93] What is the meaning of ‘stimulation’, in this context?

[L94] “Support” needs the word ‘less’ added in-front, to read ‘less support’.

[L99] “Also true for other genders”. This feels tokenistic. Suggest that either the paper should address the biases experienced by other genders, or this should be removed altogether as it is not the focus of the paper to look at non-binary contributions to, or the biases experienced by non-binary people, in academia.

[L123] Methods. It would be beneficial to know the authors reasons for selecting the field of the ecology and specifically the journal of ecology, as case studies. Are the authors from this field? Do they expect this field to be representative of the wider STEM discipline?

[L130]. “Moreover” can be removed, as presumably literature in physics is also dominated by male scientists mainly from North America and Europe.

[L140] The gender checker process seems robust.

[L146] How many papers were dismissed using this process?

[L158-L167] Gender data overview. The descriptive statistics are well set out and easy to comprehend.

[L209]. Unbiased simulation. This section needs clarity: Did the authors start with fixed ratios of women and men in each authorship list, per paper, and randomise within set authorship lists; or did they compile all female and male names together and then run simulations across all lists combined?

[L224] Define reason for splitting the data set in two subsets: presumably to avoid dependency in the data?

[L252] Typo ‘o’ should be ‘or’.

[L258] Interesting to read that the most common paper type was authored by two males. This could be set in more context of the other common paper types (number of authors and composition).

[L281] Are there any statistical results to show an increase in women’s authorship over time? The trend stabilising in a ratio of women/ total authors around 0.3 and 0.35 is confusing. Do they mean a ratio of women:total authors of 0.3? Why is it 0.3 and 0.35?

[L356] The argument that the last author should not be attributed higher contribution because even if he/she is a senior member they will have more distributed time investment is confusing. The assumptions in this argument for attributing the final author the smallest contribution are as follows: (i) where last author is senior, (ii) seniors will be contributing to many works, (iii) therefore their time is distributed, (iv) therefore they have given the least amount of time to this work. At no point have the authors defined ‘contribution’ as synonymous with ‘time investment’. Contribution can come in many forms; research conception, design, data interpretation, drafting, revisions and guidance to all of these. The time taken to contribute to each of these areas may vary, so the argument that a senior author contributed less because their time is assumed to be more taxed is not strong. Contribution’ is not well described at any point, other than being reflected by author positions in the WCI.

[L367-L371] Discussion of temporal trends would benefit from statistical tests showing the significance of changes in total authorships and women’s authorships over time.

References

Fox, C. W., Ritchey, J. P., & Paine, C. T. (2018). Patterns of authorship in ecology and evolution: First, last, and corresponding authorship vary with gender and geography. Ecology and evolution, 8(23), 11492-11507

Weltzin, J. F., Belote, R. T., Williams, L. T., Keller, J. K., & Engel, E. (2006). Authorship in ecology: Attribution, accountability, and responsibility. Frontiers in Ecology and the Environment, 4(8), 435–441. https://doi.org/10.1890/1540-9295(2006)4[435:AIEAAA]2.0.CO;2

Duffy, M. A. (2017). Last and corresponding authorship practices in ecology. Ecology and Evolution, 7(21), 8876–8887. https://doi.org/10.1002/ece3.3435

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10.1371/journal.pone.0307813.r002
Author response to Decision Letter 0
Submission Version1
3 Jun 2024

Rebuttal letter for PONE-D-24-00453

Over twenty years of publications in Ecology: Over-contribution of Women reveals a new dimension of gender bias

Dear Editor:

Each reviewer's comment is addressed individually, providing detailed responses and explanations for the changes made.

Reviewer #1: This manuscript investigates an important question - that of gender bias in publications in a STEMM subject - and introduces a new index to test for gender bias in the contribution to publications. The measure can easily to be extended to examine other biases, and is extremely useful. The dataset is impressive and the results are important and shocking, if unsurprising. The authors argue convincingly that they detect over-contribution by women, and link this to the inherently inequitable structure of the system we work in.

My only question, as I read the manuscript, is addressed in the discussion: the implications of the last author position being more regarded as of higher value than most other positions, at least in some cases (https://doi.org/10.1002/ece3.3435). The authors could present data on last author gender, perhaps compared with first author gender, but given that they include their data and code, others can explore these patterns, so this isn't necessary here.

I appreciate the clear statement of the limitations of the methods on p7.

Our response: Thank you very much.

Overall the manuscript is very easy to read. I recommend:

1. Ensuring that the figures are legible in greyscale

Our response: Done

2. Checking that the figure captions match the figures, and that the text matches the figures. I could not match the text in LL290-3 to Fig 3, for example. The measured WCI in the text is 1307, but this doesn't match the violet line in Fig 3A.

Our response

We are deeply sorry for this big mistake and thankful to the reviewer for pointing this out. The error proceeds from a non-well-checked mix of versions of our manuscript. In an earlier version with a smaller dataset, the total sum WCI was 1307, whereas in the current version with the full dataset, the total sum WCI is 1456. We corrected the value in the manuscript and double-checked there were no other mistakes like this one in the results and figures. Furthermore, for more clarity, we included in the manuscript a table with key summary values (Table 1).

Now the text is written: “We measured the WCI for each paper in our dataset and found that the total sum value for all papers was 1,456. Upon conducting the randomized simulations, we obtained a simulated WCI distribution with a mean value of 1,361 and a standard deviation of 7.75. Notably, even the maximum value in the WCI distribution after running the 10,000 randomized simulations (1,388) did not exceed the observed value of WCI (vertical violet line in Fig. 3A).”

Can you direct the reader to a figure that shows that women's contributions stabilize at 0.3 to 0.35 (L282)?

Our response: It referred to figure 2 D (bottom) We rephrased the sentence to: “Starting around 2012, the ratio of women to total authors appears to stabilize around 0.3 to 0.35”

3. The results of the KS test are missing (L316).

Our response: We now included the results in the text: “Furthermore, using a Kolmogorov-Smirnov test, we found that the compared distributions were significantly different (D = 0.13971, p-value < 2.2e-16).”

Dear Joanna Setchell,

We are very grateful to you for the thoughtful comments you made about our work. We took almost all your suggestions and appreciated your careful reading of our paper, feeling that they were raising the quality of the manuscript.

The Last Author Issue (this response is replicated for both reviewers as highlighted the same point to us)

As you can imagine, assessing the contribution of the last order became a controversial issue, igniting an intriguing internal debate within our research team. After considering other methodological options we concluded that taking the last author as the one who contributes the least is the best option, although not a perfect one. We argue that this methodological decision is the one that deals with less problematic assumptions.

We have categorized the potential errors based on how we assess the last author's contribution: either assuming the last author contributed more than the preceding authors (Type A) or assuming they contributed the least (Type B).

Type A Error:

This error occurs if we underestimate the last author's contribution, considering it poor when, in fact, they might be:

A.i: A senior author who has contributed at least more than the preceding author on the list.

Type B Error:

This error occurs if we overestimate the last author's contribution, considering it significant when they might be:

B.i: A gifted author (details provided below).

B.ii: A guest author (details provided below).

B.iii: The one who contributed the least.

B.iv: Someone positioned last due to their surname's initial letter being later in the alphabet than the preceding authors'.

B.v: An author randomly positioned last.

Given these scenarios, the most error-avoidant decision is to consider the last authors as having the least contribution. By not assuming that the last author is a senior author, we risk the opposite error: undervaluing their actual contribution. However, in cases where the last author is indeed a senior author, this issue is less severe since senior authors are typically very busy individuals whose time is divided among many projects. It is important to remember that the WCI aims to capture time investment accurately.

(We have now reinforced this idea for greater clarity in Materials and Methods: “We aim to capture the authors' degree of contribution to a paper in terms of time investment”).

The following items explain our arguments regarding the inconvenience of assuming the last author consistently guides the research teams and contributes more than the previous one in the list.

1) Temporal trends regarding the last position occupancy. Our data are dated from 1999 to almost the present. Historically, there was consensus that the contribution was decreasing in the list of authors. Only recently did the latest authors take center stage, being praised as seniors. Currently, both strategies coexist. Considering the last author as a senior is not parsimonious enough.

Duffy et al. (2017) found in a survey that most ecologists view the last author as the “senior” author on a paper (i.e., the person who guides the research group in which most of the work was carried out). However, there was substantial variation in views on authorship, especially corresponding authorship. In 2016, the corresponding author was usually the first author (range across the four journals: 77%–90% of papers); less commonly, it was the last author (range across the four journals: 9%–18% of papers.

In the manuscript, we state: “The last author may or not reflect an advisory role. Nevertheless, a co-authorship position may also imply other or even arbitrary decisions (...)”

2) More than one senior author may be co-authors in a paper. Under this situation, it is not easy (even impossible with our data) to inquire regarding the positions of multiple senior authors.

3) It is difficult to establish at what number of authors the last author becomes a significant contributor. We wonder at what number of authors does the last one become a considerable leader?

4) The Gift Authorship and Guest Authorship problem:

Laboratory group leaders or other senior academics are prone to be gifted authors. Authorship gifting happens when an individual is acknowledged in a study but doesn't meet the criteria for authorship. This practice is also referred to as honorary authorship. Essentially, it's a gesture; the individual doesn't qualify as an author per se.

The Guest Authorship. The laboratory group leader or another senior academic is prone to be gifted authors. Guest authorship occurs when influential individuals "loan" their name to a study to enhance its credibility. Nevertheless, these individuals were not directly involved in the research itself. One of the main drivers of guest authorship stems from the hierarchical organization of contemporary laboratories. For instance, principal investigators frequently require that their names be included or listed first on research conducted within their department or laboratory. They assert this demand based on their acquisition of research funds or their provision of top-level supervision. Both Gift Authorship and Guest Authorship are related to the Matthew effect. Both gift and guest authorship are commonly accepted by the actual authors partly because it's widely understood that their paper stands to benefit in terms of acceptance if they include the name of a renowned researcher. This situation adds extra noise to our attempts to understand roles in academia given that many laboratory heads and senior academics are male, they are overrepresented given the glass ceiling of the leaky pipeline.

Alternatives adjustments to the WCI: Our approach follows the Harmonic Allocation of Authorship Credit proposed by Nils Hagen in 2008 (doi:10.1371/journal.pone.0004021) for measuring the contribution of multiple authors in a paper. What we contribute to building the WCI is tallying only the contributions made by female authors; in other words, other researchers might employ different methods to measure contributions in multi-authored papers and calculate the WCI. We invite future studies to propose enhancements in index construction. As possible correction can still use some version of the Harmonic Allocation of Authorship Credit (Hagen, 2008).

If we assume that the last author is the team leader of a particular paper: Which would be the best way to value their weight? Equal to the first author? Equal to the second author? Both possibilities may be ok. Another possibility could be to consider the weight of the last author as the average weight of all other authors, and then distribute the remaining weight among the remaining authors using harmonic decay.

Hagen (2008) discusses the possibility of including additional byline information about the equality of some co-authors' contributions, or implicit information about the approximate equality of contributions by the first and last authors. Such variations are easily accommodated by a harmonic counting scheme with little or no alteration of the credit allocated to the remaining coauthors (see Hagen Figure 5). Although the Harmonic Allocation of Authorship Credit can deal with, for example, first and last authors equally merited (proposed by Hagen itself), we still think our study case prevents us from doing so. For further clarifications see arguments in Hagen (2008).

We agree that the corresponding author information is valuable. Frequently used to indicate in which situations the last author is the team leader; unfortunately, we did not register them in our data set. Future research may include them after a few changes in the Women Contribution Index. Nevertheless, due to Duffy (2017) finding that 84% of papers published in 2016 had the first author as the corresponding author, we think that our results are somehow capturing the corresponding author's trends.

-To clarify this last author issue we have reinforced the argumentative line in the manuscript section: Measuring gender inequities (Discussion).: “We have categorized the potential errors based on how we assess the last author's contribution: either assuming the last author contributed more than the preceding authors (Type A) or assuming they contributed the least (Type B). Type A error occurs if we underestimate the last author's contribution, considering it poor when, in fact, they might be: A.i: A senior author who has contributed at least more than the preceding author on the list (Duffy, 2017). Type B error occurs if we overestimate the last author's contribution, considering it significant when they might be: B.i: A gifted author; B.ii: A guest author; B.iii: The one who contributed the least; B.iv: Someone positioned last due to their surname's initial letter being later in the alphabet than the preceding authors'; B.v: An author randomly positioned last. Given these scenarios, the most error-avoidant decision is to consider the last authors as having the least contribution (Tarkang et al. 2017; Fernandes et al, 2020). By not assuming that the last author is a senior author, we risk the opposite error: undervaluing their actual contribution. In those cases in which the last author acts as a group leader, she/he may be contributing to many works in parallel and thus their time investment must be distributed (.....).Moreover, the index could be customized to suit the specific question of interest or different assumptions of author inclusion and allocation based on additional information (see [36]), for further arguments on how to value the author's contribution see the S2 Text.”

We have included a Supporting Information text (S2 Text) that further discusses the last author's issue and where we propose some alternative ways of considering the Last Authors.

An extra discussion regarding the last author:

Fox, Ritchey, and Paine (2018) measure the proportion of the last author based on gender, assuming the last author is a senior. They based this assumption on the paper of Duffy, 2017. They found that Women were less likely to be last (for them considered to be “senior”) authors (~23%) and sole authors (~24%), but more likely to be first authors (~38%), relative to their overall frequency of authorship (~31%).

Grosso et al., 2021 also found a similar pattern in the field of Herpetology. This recurrence of patterns seems to show a non-random process behind who occupies the last author position. If we assign the last authors with great value, probably the value of the WCI will be lower than what we found. Even so, given that the value of WCI that we found is larger than the highest random value in 10,000 simulations, probably the value of WCI observed if we considered the last car with the highest values would most likely still be higher than the value expected by chance. Nevertheless, it is important to take into account that, by doing so we also would be introducing too much noise to sour analysis. Gift authors and Guest authors will probably occupy the last authorship. Here we reinforce the idea that our index measures the time investment in the papers.

I also have some very minor comments on the clarity of phrasing.

Our response: All the following minor comments were incorporated. We only added clarifications itemized for those comments where it is appropriate (see below).

L47: 'academia' (no initial capital)

L54, L57: 'with' works better than 'vs.'

L58: should Men's contribution index be in italics, to match L51?

L66: I think over-contribution has been explored, but not quantified.

L76: 'this' not 'it'

L111: no need to refer to the discussion here.

L116: 'published in the journal Ecology' will do here

L117: a Science, Technology, Engineering, and Mathematics (STEM)

L188: 'reported' not 'remarked'

L134: 'within the discipline' or 'within the field' but not both

L145: 'excluded' rather than 'dismissed'

L166: should 'Publication instances' also be in bold, like other variables?

L189: 'Figure 1 shows an example' is all you need

L219: cut 'Noticeably'

L231: do you need the full explanation of qq plots? I think you can remove LL231-38.

L245: 'in' not 'under'

L266: no need for 'only'

L309: cut 'Furthermore'

L313: replace 'In the case where' with 'If'

L322: cut 'sex-segregated' because it's not quite accurate

L328: no need for 'and we discuss ...' because this is the discussion

L344: 'pondered' is not the right word here

L381: replace 'during the analysed time-lapse' with 'during the period we analysed'

L387: 'found' not 'registered'

L391: 'The dearth of women'

L392: no need for the italics

L431: maybe 'psychologist Alfred Adler'?

L437: cut 'sometimes' because you have 'may'

L444: 'classical' isn't quite right here /// Our response: It was changed by “In their seminal study, Moss-Racusin et al. [19] demonstrate that”

L454: cut 'side'

L458: 'the bias we found' not 'the regis

Attachment Submitted filename: Rebbutal letter PlosOne R1.docx

10.1371/journal.pone.0307813.r003
Decision Letter 1
González Brambila Claudia Noemi Academic Editor
© 2024 Claudia Noemi González Brambila
2024
Claudia Noemi González Brambila
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
12 Jul 2024

Over twenty years of publications in Ecology: Over-contribution of Women reveals a new dimension of gender bias

PONE-D-24-00453R1

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The authors addressed all of the initial review comments very well. Their responses were thoughtful and detailed. They have provided an in depth justification for why they are not changing the weight of the final author - which addresses the key criticism from both reviewers. The final manuscript has addressed many areas that were previously unclear and has corrected a few errors reporting statistical values. The addition of Table 1 is useful, although the formatting is not the easiest visually to interpret.

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10.1371/journal.pone.0307813.r004
Acceptance letter
González Brambila Claudia Noemi Academic Editor
© 2024 Claudia Noemi González Brambila
2024
Claudia Noemi González Brambila
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
2 Aug 2024

PONE-D-24-00453R1

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