>>Sorry for the hold up.  Before we embark on this exciting and long overdue project that we’ve collectively gathered to bring to fruition, let us begin by acknowledging that we occupy stolen lands.  The land I’m calling in from today historically belonged to the Abenaki Confederacy, the speakers of the Abenaki  language, and is now referred to as Hanover, New Hampshire.  Please refer to the link in the chat to take a moment to reflect on where you are joining us from today.  As we recognize past harms, let us also resolve to leave more equitable and humane futures behind us. 

Hi everyone, my name is Dani Bassett  and I’m a graduate student at Dartmouth College.  On behalf of all of the students and postdocs in the organizing committee, I’d love to warmly welcome you all to the first talk in the Innovators in Cognitive Neuroscience series.  Collectively, we represent a consortium of schools; Dartmouth, Harvard, UPenn, Yale, Princeton and MIT, devoted to highlighting innovative advances in cognitive neuroscience.  We acknowledge that these are lead institutes of higher learning, and to de-silo the generation of knowledge within the ivory tower, we plan to make these talks publicly available with the content of the speakers and with closed captions on the website for the doc series and on the website for cognitive neuroscience at Dartmouth College. I’m going to hand it over to Sasha to introduce this doc series.

>>BRITSKIE:  Hi everyone, my name is Sasha Brietzke, and I’m also a graduate student at Dartmouth college.  This series, the innovators in cognitive neuroscience recognizes outstanding research conducted by historically underrepresented groups in cognitive neuroscience and related fields, including women, BIPOC, LGBTQ+, and differently abled individuals. And I just want to acknowledge that we have not made our life talks fully accessible today to those who are hearing impaired as was generously pointed out to us.  We apologize for this and like  [indiscernible] said, we will provide closed captions for this recorded talk and we are working with accessibility services to provide live close captions for future talks in this series. 

We also recognize that often our work does not directly address social issues impacting and organized groups in society at large.  So to put the innovative in the ICN, we have asked all of our speakers to nominate a charity of their choice. As you can see on this screen here, our charity this week, supporting transgender equality. And we ask that everyone who attends this talk considers donating to this charity if they are able to today.  And together we hope we can bridge our science to the myriad of pressing social issues that are entrenched in our society.  And I’d like to close by specially recognizing Jim Haxby, who has generally provided the funding for this series through Dartmouth’s Center for cognitive neuroscience.  And to Courtney Rogers who has been instrumental in helping organize this series and bring it to fruition.  And on that note, I will pass things over to Jeni who will introduce our speaker today, Doctor Danielle Bassett.

>>STISO:   Hi everyone, my name is Jeni Stiso, I am a grad student in Dani’s lab and I am super excited to get to introduce her today. Dani got her PhD in physics from the University of Cambridge working with Ed Bomar and Thomas Duke before moving on to a postdoc Jean Carlson at UC Santa Barbara.  In 2013 she started her lab at Penn, where she is now a full professor.  Her lab has tackled a lot of really interesting problems.  I think it’s difficult to summarize all of the work in one sense, but I think some of the most innovative work the lab has done in cognitive neuroscience has brought theories and tools from physics and engineering to these questions about trying to understand the brain and behavior.  Dani has had tons of awards recognizing her for this work, including a MacArthur grant.  More recently, Dani has started to use her voice to advocate for diversity and equity in STEM and academia which is going to be the focus of the talk today. On top of all these really amazing scientific contributions, I can also say that Dani is a really fantastic mentor and a really great advocate for all of her students.  And with that, I will hand it over to Dani to get the talk started.

>>BASSETT: Thank you so much, Jeni.  And thank you for everybody else for organizing this series.  It says that I can’t share the screen while other participants are presenting or sharing.  So maybe if whoever has that slide pulls it down, that might help. Great, let’s see how this works.

So I am giving the talk today in support for the National Center for Transgender Equality, and so I wanted to just take a moment to tell you why, and then I will get to the scientific portion of the talk.

So transgender people are people whose gender identity is different from the gender they were thought to be at birth.  Transgender people make up .5 to .6% of the US population, so 1.4 million adults, relatively consistently across age groups.  So .66% in the 18 to 24 year old, .58% in 25-64 year olds, and .5% of people 65 years of age and older. I have highlighted four different transgender scientists in academia on the left-hand side here.  Professor Ben Barres, a transgender man, neuroscientist at Stanford University, recently deceased.  Doctor Lynn Conway, transgender woman, computer scientists and engineer at IBM.  Doctor Izzy, transgender woman, bio technologist at University of Sheffield.  And professor Andrew Perfors, transgender man, cognitive scientists at U Melbourne.  Just a few to give you some of the faces you may see in the future.

The reason I wanted to devote this talk in support of the National Center for Transgender Equality is that transgender people face pervasiveness treatment and violence.  54% of K-12 transgender children and youth were verbally harassed in the past year.  This was a 2017 documentary or survey. Because they were transgender, 24% were physically attacked, 13% were sexually assaulted, 17% experienced such severe mistreatment that they left the school.  10% of those who were out to their immediate family experienced violence from a family member because they were transgender. -- Adults reported being fired, denied a promotion, or experiencing some form of mistreatment in the workplace due to their gender identity or expression.  In the year prior to the survey, 46% were verbally harassed, 9% were physically attacked because of being transgender.  In that same period, 10% were sexually assaulted and nearly half, so 47% were sexually assaulted at some point in their lifetime.  A majority of transgender people who are murdered or black women.  

Transgender people also experience severe economic hardship and instability.  Nearly one third, so 29% of respondents were living in poverty compared to 12% in US populations, so almost 3 times the amount of the average population.  A major contributor to the high rate of poverty is likely respondent’s 50% unemployment rate, three times higher than the unipotent rate in the US population at the time of the survey, which was 5%.  Respondents were also far less likely to own a home, with only 16% of respondents reporting homeownership, compared to 63% of the US population.  Even more concerning, nearly one third, so 30% of respondents have experienced homelessness at some point in their life, and 12% reported experiencing homelessness in the year prior to completing the survey because they were transgender.

Third, transgender people experience harmful effects on physical and mental health.  A staggering 39% of respondents experienced serious psychological distress in the month prior to completing the survey, compared with only 5% of the US population.  So that’s an eightfold difference.  Among the starkest findings is that 40% of respondents have attended suicide in their lifetime, nearly 9 times the attended suicide rate in the US population which is 4.6%. Respondents also encountered high levels of mistreatment when seeking healthcare.  In the year prior to concluding the survey, one third, so 33% of those who saw healthcare provider had at least one negative experience related to being transgender, such as being verbally harassed or refused treatment because of their gender identity.  Additionally, nearly one quarter, so 23% of respondents reported that they did not seek the healthcare they needed in the year prior to completing the survey due to fear of being mistreated as a transgender person.  And 33% did not go to a healthcare provider when needed because they could not afford it. 

The National Center for Transgender Equality is devoted to changing laws, policies, resources, and society to improve trans people’s lives.  It offers efforts at the national level to fight back in inappropriate legislation that discriminates against transgender people.  It has actions at the state level and healthcare devoted legislation.  There are also efforts just to raise the voiceless of people who are transgender and provide support for families that have transgender children.  There are also significant efforts in local schools to fight for respect for policies for the children who attend them. With the important efforts the National Center for Transgender Equality pursues, I would encourage you to donate in support of this talk.

With that, I’d like to move on to the science.  I’ve titled this talk (In)citing Action to Realize an Equitable Future. The first word is a play on words, because we are going to be talking about citations, and I’ll tell you a little bit more about that as we go on.

But first what I wanted to do was to read to you this passage from Benson’s Rosetti.  Benson is one of my favorite biographers and he wrote a biography of Rosetti and he says, “We find such lines (in Rosetti) as the ‘unfettered, irreversible goal,’ or ‘Sleepless with cold commemorative eyes.’  Note such textures as -- ‘oh, what is this that knows the road I came, the flame turned to cloud, the cloud returned to flame, the lifted shifted steeps and all the way? -- that draws round me at last this wind-warmed space, and in regenerate rapture turns my face upon the devious coverts of dismay?’ Or, and he quotes Rosetti again, ‘Ah! Who shall dare to search through what sad Maize thenceforth their incommunicable ways follow the desultory feed of death!’”

And now Benson is speaking again. “It will be observed in these last quotations there is a certain slight shifting of the usual meaning of words like commemorative, regenerate, and incommunicable, some slight nuance added to them which is not found in ordinary speech.  This preciosity has a charm of its own, and upon mishandling of language, this delicate straining of the use of words, depends much of the pleasure derivable from the work of masters of elaborate style.”

I like this passage because it suggests to me that words that we use can be stretched in their meaning.  They can change their meaning, they can reconfigure their meaning, maybe there is some amount of energy that’s required to allow them to change that meaning.  Imagine there’s a word that’s being stretched or flexed to another word.  There’s some amount of energy that’s required to do that.  And then that word may be stretched in a new meaning direction, and there’s an amount of energy that’s required to do that.  And again.  I think this notion of words stretching their meaning makes me think about what are the drivers of change in meaning and change in concept? What factors can and do drive changes in meaning and concept?

Certainly as shown in the Benson passage, just a short section of a book or a short section of an essay can push or pull the meaning of words just by the words right around it.  But that happens over very short periods of time.  There’s also ways that meaning can change over longer periods of time, in, for example a book.  I highly recommend Ben Barre’s autobiography; fantastic work that really stretches the mind in terms of the meeting that has been associated with specific concepts. And then culture can also change the meaning of words.  In particular I really enjoy Cultures Without Culturism: The making of scientific knowledge, by Karine Chemla and Evelyn Fox Keller. Across these three instances, earlier sense of not appropriate to the situation.  

In science, I want to give you an example of not a word whose meaning has changed, but a word whose meaning needs to change.  And that is the meaning of the word scientist.  In our popular conception, the word scientist has the meaning of appearing masculine. So there’s this wonderful paper from 2016 from Banchefsky et al. that showed pictures of actual faculty members in STEM at elite universities to a set of participants.  Those participants rated the pictures in terms of masculinity and femininity.  Then a separate group of participants was asked to rate the likelihood of the person being a scientist. The participants assumed that feminine looking women were early childhood educators whereas more masculine looking women were more likely to be scientists. That same variation across the masculinity/femininity spectrum and its relationship to career was not found in many categories. 

The meaning of a scientist not just appears masculine, but also is a man.  In this paper from  [indiscernible] science in 2019 from Dutt et al., there were 1,224 letters that were considered.  They were written for 452 applicants averaging about 2.71 letters per applicant.  By 1,101 commanders from 54 countries over the period 2007 to 2012. What they found is that the woman applicant was only half as likely to receive excellent letters versus good letters.  Man applicants were rated as brilliant, rising stars, pioneers, geniuses, and trailblazers and women applicants were described as having a solid skill set, a good track record, strong knowledge, being intelligent, and having an aptitude for learning.

Finally, the meaning of the word scientist doesn’t just connote an appearance of masculinity or being a man themselves, but also having a man gendered name.  In a randomized double-blind study of n = 127, physics, biology, and chemistry faculty from research intensive universities, they rated the application materials of a student who was randomly assigned a name commonly given to either a man or woman for a laboratory manager position.  If the application was assigned a woman’s name, it was deemed less competent, less higher-level, offered less mentoring, and offered $3,700 less in salary than an identical application that was assigned a man’s name. I want you to note that this is not the privilege of a gender.  This is the privilege of a name alone.

And with that, I think we need to broaden out.  These three studies are only a few of the many I could’ve brought you today indicating the meaning of the word scientist to us as a group, as a populace.  Prior work has reported gender and also racial inequalities in academia and industry that clearly support those previous studies.  There is reported gender and racial inequalities in compensation, in grant funding, in credit for collaborative work, in teaching evaluations, hiring and promotions, productivity and authorship, and citations. What’s really interesting about the conversation about gender and racial inequalities in academia and industry is that conversation still focuses largely on the role of people in positions of power.  So journal editors, grant reviewers and agencies, department chairs, and society presidents. Despite the fact that many imbalances are caused and perpetuated by researchers at all levels.  Including you and me.

One particular example of those inequalities that are perpetuated by us is the problem of citations.  People from marginalized groups are broadly under-cited by everyone, impacting visibility, slowing or stalling career advancement, and precluding an unbiased trajectory in the search for scientific truth.  There is clear evidence in the scientific literature that we choose new research directions from highly cited papers.  There’s also clear evidence that papers written by men are cited --

We assign the term man or woman to each name if that name had a part ability greater than or equal to .7 of belonging to someone labeled as man or woman according to the Social Security ministration database or the gender API. And here I want to be very clear in the distinction between sex and gender.  The way we are using the term gender does not relate to the sex of the author.  When I say the gender of this person, I do not refer to any of their sexual physiological characteristics as assigned at birth or chosen later.  The term gender as I am using it also does not directly refer to the gender of the author as socially assigned or as self chosen.  When I use the term gender in this study, we are referring to the probability of a name being used by a man or a woman.

Second, I want to underscore a significant limitation of the work about to show you, and that is that we are using a man/woman binary gender assignment, and this is not well accommodated to intersexed, transgender, and/or non-binary identities. We are engaging in ongoing and future work to address this limitation.

Just to set the stage, if we take all of those 61,000 some articles across the five top neuroscience journals, which are Nature Neuroscience, Neuron, Journal of Neuroscience, Neuroimage, and Brain, here you can see the proportion of papers that were written by having a man first and last author in 1995, so about 64%.  And then up through 2018, see you can see it’s about 50% in 2018. 

You can also see the number of papers that are written by mixed teams, so woman first author and man asked author, or man first author and woman last author.  And finally, you can see the number of papers that were written with the first author woman and the last author woman.  It started at 7% in 1995 and is up to about 10% in 2018.  That’s an increase of roughly .6% per year.

In this data set, we first wanted to test the hypothesis that had been motivated by prior work in literature; do we see an under-citation of women? To address that hypothesis, for each of 31,418 exciting papers between 2009 and 2018, we took a subset of its citations that had been published in one of those five journals and’s 1995 and we determined the gender of the cited first and last author.  We also removed sulci stations, which we defined as cited papers for which either the first or last author of the citing paper was the first or last author.  That helped us to really evaluate how each author was canvassing the field, not canvassing themselves.  Although, in the supplement we do report the effects of self citations.

Finally we collected the number of cited papers that fell into each of the four first author and last author categories. So man/man is referred to as MM, woman/man is WM, man/woman would be MW, and then woman/woman I'll refer to as WW. 

So next we need to estimate over and under-citation. How can we quantify whether or not one group is being over cited or under-cited. Now an actual place to start would just be to assume that when we construct our reference list we're taking a random draw from the literature and therefore we're more likely to be citing men because there are more men led papers, and we're less likely to be citing women because there are fewer papers written by women. 

So to sort of implement that random draws approach, what we did is we calculated the gender proportion among all papers published prior to the citing paper, thus representing the proportion among the pool of papers that the author could have cited, and multiplied them by the number of papers cited. So here you can see the results of percent of over and under-citation. If the value is close to zero, that means you are citing in proportion to what's in the field. So you will have more citations to papers that have a man first author and man last author just because that's the proportion in the field. If you are above zero, that means you are citing those kinds of papers more than expected given their proportion in the field. And if you are under zero, it means you are citing those papers less than expected given their proportion in the field.

We define this over and under-citation as the observed percent minus the expected percent, then divide it by the expected percent. What we found is that man/man papers were over cited by 11.6%, woman/man papers were under-cited by 10%, man/woman papers were under-cited by 12.5%, and woman/woman papers were under-cited by 30.2%. Sort of a whopping one there. 

Now, you might say that when you cite -- when you construct your reference list, you don't necessarily take a random draw of papers and stick it in your references. Maybe there are some other reasons that you would cite a paper and we completely acknowledge that. So in our next known model to estimate over and under-citation, we included several important paper characteristics that we thought were very relevant to the expected citation. That includes the year of the publication, the journal in which it was published, so we know the impact factor of the journal can affect whether or not that paper will be highly cited or less highly cited. 

Number three, the number of authors on the paper. Number four, whether the paper was a review article. They tend to be cited more than an empirical article. And five, the seniority of the paper's first and last authors. We don't know the age of the people in this group, so as the proxy for seniority, we counted the number of papers they had previously published. So we use their number of papers as a proxy for their seniority. 

Here you can see the percent over and under-citation under this relative characteristics model. What you can see is that man/man papers are over cited by 5.2%. Women man papers are under-cited by 6.7%. man/woman papers are under-cited by 4.6%. And women women papers are under-cited by 13.9%.  So you can see the overall trends still hold, although they are not as marked as they were in the random draws model. 

The second hypothesis that we wanted to test is that the under-citation of the minority is driven by the majority. And again that's motivated by prior work that I don't have enough time to get into today. But what we did is that we separated out all of the papers that are written by a man first author and man last author, and the papers that have a woman in either the first and last author position. And we just showed the citation practices. So here are the citation practices of the man/man teams. What you can see is that there's a consistent over-citation of men, a consistent under-citation of women women papers. 

If you look at papers that have a woman as a first or last author and look at who they cite, they cite much more equitably across the groups. In fact, in this case they are over citing the man/man papers by 2.5%. They are under citing the women women papers by 4.2%. So this suggests that the imbalance is largely driven by the citation practices of the man/man teams.

Now I want to note a few points on logic. Number one, the fact that the imbalance is driven largely by the citation practices of man/man teams does not mean that all men led teams have imbalanced citation practices. I want to highlight the citation practices of Professor emeritus at KU Leuven, Guy Orban, who cites actually in complete opposite trend to what is expected. So he tends to under-cite man/man papers and significantly over cite women women papers. So that's just an indication that you can find a man led team or even just a single person that cites either equitably or inequitably and either in the direction against the minority or in the direction for the minority. 

The second part of logic that I want to clarify is that the fact that women teams are not driving the imbalance doesn't mean that all women led teams have balanced citation practices. So I asked Jordan to check my citation practices and it's not something that I'm particularly proud of. You can see that I do over cite the man/man papers a bit, but I heavily under-cite women women papers. In fact more than is expected in the general populous of this data set.  So that's not something that was good to see, but it is something that I can change with every paper that I write in the future.

Lastly we wanted to test hypothesis three, which is that we thought that this imbalance should be decreasing over time because we know that the number of papers that are written by the gender minority, which in this case is women, are increasing with time. So we thought, well with more papers written by women, hopefully the imbalance is decreasing with time. But we actually find the complete opposite. We find the imbalance within reference lists is increasing with time, not decreasing. And we can see that's largely being driven by the citation practices of man/man teams. 

So this is the man/man team part of the data set. Here's the percent over and under-citation. You can see that those teams tend to over cite other man/man papers at an increasing rate with time. And you can see that they're under citing women women papers at an increasing rate with time. So that the gap in the citation imbalance that is occurring in 2018 is markedly larger than what I was in 1995. If you look at the teams that have a woman in the first or last author position, what you can see is that there is a slight trend upwards, but not to the degree that you observe in the man/man teams. In the man/man teams, the rate is increasing at .54 percentage points per year. While in the reference list that have a woman in the first or last author position, that rate is .29 percentage points per year. 

The last question that we had was, what is the impact of social networks? There have been prior work that showed that researchers are more likely to work with other researchers of their own gender, and such homophily in co-authorship networks can produce biased perceptions of the overall gender make up of a network. So we quantified two different measurements of the social network man author over representation and man/man paper over representation. I'm not going to get into the details because of time and I want to get into the next section of the talk, but just to show that if you account for that social network homophily in the co-authorship, we do not fully explain the over-citation of the man/man papers. So this empty spot between the color bars, color bars are what's explained by the social network, and the empty parts are what's not explained by the social network. And what we find, therefore, since there is this empty area that local homophily explains only part of the over-citation of men by other men. 

I want to pause here for some explanations and some non-explanations. What are these data show? The under-citation of women in neuroscience papers may be due to systemic gender bias, or to explicit or implicit individual bias relative either to the known gender of an author or to an author's gendered name. The same is true for race and ethnicity. Over representation of men in course syllabi and conference speaking roles is clearly a factor in gender, racial, and ethnic disparity in academia. But this mechanism does not explain the fact that over-citation of the majority is largely driven by the majority themselves, not by everyone.

The growing gender gap, and I'll show you race and ethnicity next, does not simply reflect an author's propensity to cite older literature from when the field was more majority dominated, as the expected proportions account for the publication year of the article being cited. And lastly, the findings are not explained by self citations driven by a man dominated sub field or driven largely by a few high impact papers rather than being a systemic effect across papers of all impact levels. And you can see the supplement of the paper for evidence in favor of those three claims. 

The community's response to this paper has been overwhelmingly positive and, I don't know, they've been acknowledging that this is happening. So there was a very fascinating and supportive Brain editorial in 2020. There was an editorial from the Nature Neuroscience editors called "Widening the scope of diversity." There was a wonderful perspective from Adrienne Fairhall and Even Marder called "Acknowledging Female Voices." There's also been a generalization to the Journal of Cognitive Neuroscience that's titled "Gender Imbalance in Citation Practices in Cognitive Neuroscience." And that's on the bio archives, so you can check that out. There's also been an editorial in Nature Reviews Physics called "Growing Citation Gap." 

So, what I want to ask the question of, I started by saying that there was both gender and racial or ethnic disparities in academia, and I've really just talked about gender so far. And what we wanted to do next was to ask about other dimensions of discrimination. Is this something specific to gender or is it generalizable across other dimensions of difference? 

To answer that question, we expanded the study to consider the race and ethnicity of the authors that are being cited in this group. So to assign author race, what we did is we used a publicly available probabilistic database and a deep neural network that learns the relationships between names and racial or ethnic categories on Wikipedia entries. The approach allows us to estimate the probability distribution across four different racial categories. Asian, Black, Hispanic, and white, based on each author's first and last names. 

What we did is that we expanded the data set to 2019, and so now we have 63,677 articles. We found the proportion of articles with a person of color as first or last author significantly increased between 1995 and 2019 at a rate of approximately .97% per year. So you can see that in dark blue, we have white author white author papers, so first and last. And then this burnt orange we have author of color/author of color, so first and last. And then in the greens we have the mixed categories. 

Now let's ask the same kinds of questions. Is there evidence for over or under-citation of authors of color in the random draws model? That's when we take account of the actual proportions in the field and ask if you cite according to those proportions, are your reference lists kind of consistent with existing proportions or skewed in comparison to existing proportions? On the left hand side, I'm showing you the data for all citers. So here you can see the percentage over and under-citation to papers that have a white first and last author, up through papers that have an author of color in the first and last author position. In the light color violins, you see the known model, and then in the darker colors you see the actual data. 

So what you can see here is that all citers tend to over cite papers that have a white author in the first and last author positions by 7.1%. They tend to under-cite papers that have an author of color in the first and last author position, and that's by 17.3%. Now you can separate that data into just the write citers or the citers of color. And what you can see is that white citers over cite other papers by white people by 13% compared to the 7%, so almost twice as much as what we saw in the complete data set. And they under-cite papers written by an author of color in both the first and last author positions by 32.7%. The citers of color, on the other hand, are citing relatively equitably. So you can see that they're roughly citing according to the proportions that are in the field.

Now we can ask about the relevant characteristics models, so not just did they randomly draw papers from literature, but if we account for some characteristics of a paper that may be relevant for citations, do we still see this imbalance? The answer is yes. In this case what we did is we used the year of publication, the journal of publication, and the number of authors, the research article or review criteria, first and last author seniority, and the location of the authors' institutions. And again, here is the data for all citers on the left hand side, so you can see that all citers tend to over cite papers with a white first and last author by 5% and tend to under-cite papers with authors of color in the first and last author position by 7.6%.

On the right hand side, you can see the difference in the citation practices of white people and the citation practices of citers of color. You can see that the majority of the imbalance is being driven by white citers. They over cite other white papers by 9.2%, and they under-cite papers with an author of color in both positions at a degree of 19.6%. 

Now, is the racial and ethnic imbalance in citations decreasing in time as we would hope given the fact that there are more papers written by racial and ethnic minorities? The answer is again a depressing no. It's actually increasing with time, not decreasing. So here I've separated the data out by white citers and citers of color. What you can see is white citers are over citing papers written by a white first and last author at an increasing rate over time. And they're under citing papers with an author of color in both positions at a rate that's decreasing with time. So the gap between the citations that are given to papers with white people and papers with authors of color is widening, not closing.  And you can see the practices of citers of color on the right hand side. What you can see is that they tend to cite much more closely to the zero line, which is the dashed gray mark. There is a slight increase in over-citation of white people in this purple line, but it's a much slower trend than it is in the white citers. They also do not tend -- they are not changing in their citation of other authors of color with time. 

There are some partial explanations for these findings from coauthorship networks.  So we do find that racial and ethnic segregation in coauthorship networks is increasing with time despite greater field-wide collaboration. So that suggests that authors of color are more and more working with other authors of color in building new coauthorship networks. And white people are more and more working with other white people in building the emerging coauthorship networks.

We also find that the most imbalanced white citers tend to cite more locally within a coauthorship network then the least imbalanced white citers. So they are really signing more of their academic friends or siblings rather than canvassing the field equitably.  And most imbalanced citers of colors utilized unexpectedly long paths to reach white authors, passing over authors of color close to them. So this does suggest that coauthorship explains some of the citation practices of both white citers and citers of color.

The last data question we had was what about intersectionality? What is the cost that is paid by someone who is an author of color and is also a woman? So here we see in color is a percent of over and under-citation.  It along the X axis is the last author’s resource ethnicity and gender, men first, then women second.  And then along the y-axis, you see the first author’s race and ethnicity, men first and women second. What you can see by looking at this is that the first principal component of this matrix is a component of gender.  On average, papers that have a man first and last author are under-cited independent of their race or ethnicity.  And on average, papers that had a woman in the first and last author position are under-cited hugely independent of their race and ethnicity.

After you take into account the first printable component of gender, then we see variation according to race.  So if in the man/man block you see that papers that have a white man as first and last author are over cited much more than a paper that has a black man as a first and last author, which is over cited by 1%, which is not huge.  It’s almost at the equitable line. And then here what you see in the papers that have a woman as first and last author, that the largest cost is paid by Hispanic women and black women.  And again, remember that these effects are largely driven by the majority race which is white and the majority gender which is man.  And they are increasing with time, not decreasing.

So the big question is, where do we go from here? And I think we need to very carefully grapple with what are the ethics of citation practices? I will be the first to acknowledge that the ethics of safety practices remain to be further defined.  Writing social inequalities can be accomplished under a couple different ethical models, and I want to talk about at least two of them.

The first one is the distributional model.  In this model, justice refers to the morally proper distribution of social goods and resources.  Or, in this case, citations.  And on the equality based distributive model, citations ought to be allocated to all authors equally.  While on the equity based distributive model, citations are to be allocated by authors differentially based on select factors which might include merit or need or authority. I will note that the distributional model is severely limited in my opinion by emphasizing commodity parity across economies of exchange over differential responsibilities for the histories and structures of inequality.  I, therefore, am more supportive of different models of ethics, which by contrast recommend acts of reparative justice which may include affirmative action in citation practices, institutional reform to support citation parity, and disciplinary redress of gender bias and racial ethnic bias more broadly.

Let’s ask, once we have defined what the ethics is, what are some concrete recommendations? We wrote a paper that was recently published in Neuron that is more of a perspective piece on the work rather than an empirical piece.  In that, we create some recommendations for creators of science, arbiters of science, and reflectors of science.  For the creators of science, those are producers, generators, authors, designers, and originators of manuscripts.  They can be undergrads, research assistants, grad students, postdocs, research staff, or PIs.  In this group, ask yourself whether your reference lists are balanced.  Educate yourself on the work of minority scientists.  Gather information about the current gender, race and ethnicity distribution of authors in your field.  Determine whether you aim to reflect that current distribution or to proactively cite more work by minority scientists as an act of affairs of justice and affirmative action in citation practices. Number 5, consider appending a citation diversity statement to your paper.

What is a citation diversity statement? A citation diversity statement looks like this and we have another paper with Nicole Rust in 2020 in Trends in Cognitive Science, which describes what a citation diversity statement is and how you might go about creating your own.  Here’s an example of what one might look like.  It moves from motivation, to method, to result, to limitations.

It reads like this, “Recent work in several fields of science has identified a bias in citation practices such that papers from women and other minority scholars are under-cited relative to the number of such papers in the field.  Here we sought to proactively consider choosing references that reflect the diversity of the field in thought, form of contribution, gender, race, ethnicity, and other factors. First, we obtained the predicted gender of the first and last author of each reference by using databases that store the probability of a first name being carried by a woman.  By this measure, and excluding self citation to the first and last authors of our current study, our references contain A% woman/woman, B% man/woman, C% woman/man, and D% man/man, or E% unknown.  This method is limited in that a) names, pronouns, and social media profiles used to construct the databases may not, in every case, be indicative of gender identity, and b) it cannot account for intersexed, non-binary, and transgender people. Second, we obtained predicted racial and ethnic categories of the first and last author of each reference by databases that store the probability of a first and last name being carried by an author of color. By this measure, and again excluding self-citations, our reference list contains F% author of color/author of color up through I%, white author/white author. The method is limited in that a) names , census entries, and Wikipedia profiles used to make the protections may not be indicative of racial ethnic identity, and b) it cannot account for indigenous and mixed race authors or those who may face differential biases due to the ambiguus racialization or ethnicization their names.  We look forward to future work that could help us to better understand how to support equitable practices in science.”

Why add something like this to your papers? Well, number 1, it holds us accountable.  For two, it increases global awareness of citation imbalance.  And number 3, you can point future readers to relevant tools for mitigating disparity.

Now, that’s if you are a creator and you actually have a paper.  What if you don’t have a paper yet? In that case, you may think about your responsibilities and potentiality. So, six, proactively consider fleshing out ideas from minority scholars early in the problem formulation stage so that the work of minority thinkers is, in fact, central to your questions, not an afterthought.

What about arbiters? These are mediators, moderators, and adjudicators of the scientific publishing process like lab heads, journal editors, and reviewers.  One, discuss citation diversity as a team.  Two, solicit a shared ethics in citation practices as a lab work journal policy.  Three, don’t open the gate until the paper has a reference list that the lab or the journal would be proud of. Number four, raise awareness by requiring citation diversity statements.  Number five, explicitly support the work of minority scientists by inviting them to co-author reviews and author invited papers. Number six, assess citation diversity when you perform peer-review. 

And lastly, what about reflectors? These are the people who mirror, reflect, and canvass the scientific field for others. These are mentors, instructors, and review authors.  First, they can commit to offering a balanced view of the field as it stands now and an increasingly equitable view of the field as a gross.  Number two, they can facilitate summer research programs and collaborative research between young and established URM scholars.  Three, check your syllabi for underrepresentation.  Introduce students to relevant URM work. Number four, for review authors, expand your own and other’s sense of the field by consciously including, and in some cases highlighting, work by URM scholars.

In addition to those things that each of us can do as we fall into the roles of creators, arbiters and reflectors, we as a lab, and others, have begun developing tools to help mitigate disparity.  The first tool is a citation diversity statement, but there are others as well.  So the citation diversity statement was developed by Jordan Dworkin, who was the first author and really the lead pioneer thinker in the gender paper.  And a description of that statement is in the Trends in Cognitive Science paper. In addition, we have software for the gender predictions that you can apply to your reference lists if you’d like to see what your balance looks like and if you want to address any imbalance that exists. That’s on GitHub and that’s from Dale. Jeni has just created and released a Citation Transparency Google Chrome extension. It also works with Pubmed, and it’s free on the Chrome Web store.  It allows you to predict the genders of the first and last author of the papers that you see on Google Scholar or on Pubmed.  That helps you to educate yourself on the work of minority thinkers. And last, Max Bertolero has Python code for predicting gender, race and ethnicity which is on GitHub under his title, Balanced Citer.  And that’s available if you like to consider applying it to your reference list to understand whether your papers in general have balance in terms of race and ethnicity, as well.  He is a first author of the follow-up paper, Evaluating Racial and Ethnic Imbalance in Citation Practices in Neuroscience.

With that, I’d like to quote Maya Angelou, “The truth is, no one of us can be free until everybody is free.”  I think we all have a responsibility to consider the fact that it’s time for a state transition in mind.  A scientist is not a white, cis-gender, heterosexual man. A scientist is anyone who is seeking to understand how the natural world works. Changing the meaning of the word scientist may take some energy, but it’s energy that each of us can exert.  The tools we have created put the power of social justice into the hands of each individual person on this call. 

With that, I’d like to acknowledge the people who were important in the work. Jordan Dworkin is the person who led the gender study.  Max Bertolero is the person who is leading the race and ethnic bias and balance study.  Dale created the initial finder to predict gender from reference lists. Perry Zern is a philosopher at American University who has been extremely important in helping us to contextualize our efforts in the context of current gender and race theory. Jeni is just a powerhouse in all kinds of ways, but also developed the Citation Transparency Google Chrome extension. Taki and Kristin are biostatisticians who contributed to the first paper in gender. Erin did, as well. She’s a postdoc in physics.  And then TonyToni, Damian, Kaf and Lucina are all collaborators on this paper that we will hopefully be submitting soon on the racial and ethnic imbalance in citation practices.

With that, thank you so much for listening stop I’d be very happy to take questions stop