2. Contagion of disrespect

The “devaluation view” of labor in sociology of employment states that a change in the gender distribution of an occupation will lead to a change in the valuation of the work done (Levanon et al. Reference Levanon, England and Allison2009). There seems to be a similar devaluation in science. Further, Schneider et al. (Reference Schneider, Rubin, O’Connor, Ramsey, De Block and Pittsburgh2022) describe a contagion of disrespect, where new results in fields associated with marginalized groups are increasingly dismissed as unimportant to the production of scientific knowledge. Though this phenomenon is less well-studied than the devaluation view of labor, there are a few historical episodes that serve as ready examples.

First, as described by Schneider et al. (Reference Schneider, Rubin, O’Connor, Ramsey, De Block and Pittsburgh2022), child study, which studied infant cognitive development, arguably became less respected as women researchers took over. In the 1870s and 1880s, various well-respected “men of science” like Darwin began publishing detailed accounts of their children’s psychological developments, and encouraging others to do the same, leading to a booming interest in the field of child study. However, since women had easier access to children (because they were in charge of raising them), by the 1890s, scientifically minded women like Milicent Shinn were compiling extensive notes and providing valuable findings. Correspondingly, the field diminished over the next 20 years and was eventually overshadowed by the field of experimental psychology (Lorch and Hellal Reference Lorch and Hellal2010; von Oertzen Reference von Oertzen2013).

The history of computer science arguably shows both a contagion of disrespect and a reverse case, where respect increased as it became more male dominated. In the United States, before World War 2, male “computers,” or what we would today call programmers, were given status as technical experts. During the war, however, women took over this role, and “With feminization came a loss of technical status” (Light Reference Light1999, p. 460). As computer programming rose in popularity and importance during the war, it was overwhelmingly viewed as clerical work for women that freed male engineers from tedious calculations. It was considered less productive for knowledge or innovation than male-dominated engineering, even though it required the same understanding of the hardware as well as a high level of mathematical skill. Following World War 2, women were generally excluded from programming work and encouraged to be math teachers, or some such, instead (Light Reference Light1999). Computer programming today enjoys correspondingly higher prestige than it did 70 years ago.

Though we do not have similar temporal data about changing demographic compositions, there are plenty of other fields where arguably a dismissal is/was due to their demographic composition. Just to name a couple: home economics, which among other things tied the kitchen to the chemical laboratory and allowed women to be professional academics (Stage and Vincenti Reference Stage and Bramble Vincenti1997), and indigenous ecology and forestry, where valuable work could have been integrated into other work on similar questions (Mason et al. Reference Mason2012; Kimmerer Reference Kimmerer2013). In what follows, we will tend to use the term “devaluation” to refer to the general phenomenon of scientific fields being dismissed due to their demographic composition, while referring to cases where such fields are increasingly dismissed over time as instances of a contagion of disrespect. (Though, as might already be apparent, we will use the terms “field” and “discipline” interchangeably.)

Studying the contagion of disrespect is important. We stand to lose a lot if entire areas of scientific research are dismissed, not because of any defect in their results, but because of their demographic composition. So, how do we explain the devaluation of work in these and other fields? One could appeal to particular roadblocks that exist or existed in the past, such as lack of institutional access. While those barriers are certainly an important part of the story, we want to show that, also, the social dynamics surrounding collaboration can lead to the devaluation of certain fields. Studying these dynamics allows us to look beyond particulars to see how (some) present reasons for devaluation are both continuous with the past and likely to continue into the future. Of course, here we are focusing on academic collaborations, but this viewpoint can apply more broadly.

3. The social dynamics of science

Sun et al. (Reference Sun, Kaur, Milojevic, Flammini and Menczer2013) provide an agent-based model to explore how collaboration dynamics can shape broad patterns across scientific disciplines. The basic idea is that as scientists go about collaborating and producing research, they create a network structure which captures how different scientists are connected to each other. As an overview, in each round of a simulation there are three stages:

1. 3.1. Publication: A new paper is published by a scientist, possibly with co-authors.

2. 3.2. Growth: There is some probability a new scientist enters the network.

3. 3.3. Landscape change: There is some probability fields merge together or split apart.

The model starts with one scientist writing one paper. Then, as new scientists enter the community, they enter into their own collaborations and the network builds up from there. We will go through the above three stages in more detail now.

4. Incorporating inequities

While we take the basic model from Sun et al. (Reference Sun, Kaur, Milojevic, Flammini and Menczer2013), we are interested in different questions. As mentioned, we are motivated by the observation that fields associated with marginalized groups tend to be devalued and the hypothesis that the social dynamics of collaboration are an important part of this story. So, we incorporate into the model ways that social identity can affect aspects of collaboration. ^{Footnote 3}

First, data shows that there is often an unequal division of credit within collaborations. Women and members of minority groups are less likely to hold prestigious first and last author positions, and tend to put in more work in a lab while being less likely to be given any authorship at all (see, e.g., West et al. Reference West, Jacquet, King, Correll and Bergstrom2013; Feldon et al. Reference Feldon, James Peugh, Maher and Tofel-Grehl2017). In other words, they tend to get less credit from the products of their joint work. We know something about the short- or medium-term consequences of this unequal division of credit within collaborations. For instance, it can affect who scientists choose to collaborate with, as they try to avoid unfair collaborations. Thus, inequity can affect the collaboration network within a single discipline, ultimately leading to segregation or clustering along social identity lines (Ferber and Teiman Reference Ferber and Teiman1980; McDowell and Smith Reference McDowell and Kiholm Smith1992; Rubin and O’Connor Reference Rubin and O’Connor2018). This, however, leaves open questions of the long-term consequences of collaboration inequity for the shape of the scientific community and our valuation of knowledge produced within whole disciplines.

Another aspect of inequity is the Matthew effect, where the rich get richer (Rossiter Reference Rossiter1993). In this context, there are two ways that past credit accumulated for one’s work can affect the future credit one expects to receive. First, past credit affects the probability that a person gets added onto a collaboration; high reputation from past credit makes it more likely to be asked to join projects in the future, thus increasing the amount of work for which a person gets credit in the future (Chakraborty and Chandra Reference Chakraborty and Chandra2016) Second, past credit also affects how much credit is generated by each new paper that author produces. Papers by well-known people are more likely to be widely read and highly cited (Petersen et al. Reference Petersen, Fortunato, Pan, Kaski, Penner, Rungi, Riccaboni, Eugene Stanley and Pammolli2014).

We incorporate these three factors—collaboration inequity and two aspects of the Matthew effect—into the modeling framework developed by Sun et al. (Reference Sun, Kaur, Milojevic, Flammini and Menczer2013) to show how they can affect the overall shape of the scientific community over time. We build in each feature one by one, yielding three models. In addition, we incorporate “retirement” into the models, where a scientist stops collaborating. (The node stays in the network but is no longer an active part of the network.) Unlike Sun et al. (Reference Sun, Kaur, Milojevic, Flammini and Menczer2013), we track the reputation of scientists, so including this retirement mechanism avoids distorting our view of how reputation can accumulate to a person over the lifetime of their career. To keep things simple, scientists retire after 1,000 time steps, though section 5 will discuss how we might make this more complicated.

Model 1: We start with a model incorporating collaboration inequity. We introduce “types” into the model, which do not affect anything except the credit an author receives for a paper. We assume that a type 1 agent gets one unit of credit per paper they collaborate on, where a type 2 agent gets two units. Therefore, being type 1 represents being a member of a marginalized group and receiving less credit for your work than a member of a dominant group, consistent with empirical evidence regarding how credit is often distributed.

Model 2: The next model additionally incorporates reputation, where reputation is a function of a person’s total accumulated credit. There are diminishing returns of credit on reputation—e.g., 10 new citations are worth more to someone just starting out versus someone at the top of their field—captured in the model by reputation equalling the square root of an author’s total credit accumulated. Model 2 then includes everything from model 1 plus the aspect of the Matthew effect where past credit increases chances to be added to a project; the probability to choose someone as a co-author now depends both on past co-authoring and author reputation. ^{Footnote 4}

Model 3: The final model allows past reputation to feed into future credit generated per paper. Since, again, there are diminishing returns to accumulation of credit, we say that the reputation of the set of authors is found by summing the credit accumulated by all authors in the collaboration, then taking the square root of that. Model 3 includes everything from model 2 plus the assumption that papers generate an amount of credit, c _p , according to the total reputation of the collaborators involved. In this case, type 1 agents receive 1 × c _p and type 2 receive 2 × c _p for the paper.

4.1. Contagion results

The percentage of marginalized researchers in a field predicts the average credit per paper coming out in that field. Figure 1 shows scatter-plots of all the disciplines, for all runs of the simulation, according to the representation of the marginalized group (on the x-axis, with higher numbers indicating a higher proportion of marginalized researchers) and the average credit each author in that discipline accumulates (on the y-axis). ^{Footnote 5}

Figure 1. Average credit per author vs. representation of marginalized group in each discipline for (a) model 1, (b) model 2, and (c) model 3.

Figure 1(a) shows that the more marginalized researchers there are, the less credit accumulated by the average person in that field. In particular, the slope of a linear regression is negative but small in model 1. ^{Footnote 6} That the slope is negative is unsurprising. This is essentially built into the model, as each paper produced by a marginalized researcher is worth less—they might manage to accumulate as much total credit as a member of the dominant group, but they would have to publish twice as many papers. However, the contagion of disrespect is not simply an observation that fields associated with marginalized groups are seen as less good, rather it is that they are increasingly dismissed as unimportant to the generation of scientific knowledge. Looking to models 2 and 3, the way this collaboration inequity interacts with the way reputation functions in these collaboration dynamics can start to give us a better picture of how a contagion of disrespect might arise.

As seen in figure 1(b), in model 2, the slope of the linear regression is more negative, meaning that researchers in fields associated with marginalized groups tend to be seen as even less productive of scientific knowledge than researchers in other fields. The slope is even more negative in figure 1(c); in model 3, authors generate less credit still in fields associated with marginalized groups. The scale is different for this model versus the other two—because the credit per paper varies based on the authors—making very specific comparisons of model 3 with models 1 and 2 somewhat complicated.

However, we can see from model 3 that incorporating reputation building gives rise to something we can call a contagion of disrespect. When reputation has no effect, as in model 1, results produced by disciplines associated with marginalized groups are devalued, but only due to pre-existing inequities, rather than anything to do with the collaboration dynamics that affect the evolution of disciplines. Then, when reputation is incorporated, as in model 2, the collaboration dynamics begin to come into play in terms of the possibility of people preferentially collaborating with those who happen to have slightly higher reputation. However, those people only have higher reputation if they by chance write more papers, and the number of papers any one person writes during their career is limited. There is no mechanism by which the difference between authors can substantially increase over time, or by which these high-reputation authors can increase the reputation of their co-authors, who are likely to be in their field.

In model 3, the reputation building aspect of the Matthew effect provides exactly that kind of mechanism, where the reputation of individuals and, as a side-effect, the reputation of disciplines, can increase over time. This reputation building benefits the dominant group more: credit feeds into future credit for all scholars, but members of the dominant group have more credit to feed into that process at the start. ^{Footnote 7} With the conjunction of reputation building and collaboration inequity according to social identity, we can then see the emergence of a contagion of disrespect, where new results by authors in fields associated with marginalized groups generate less and less relative credit, or are seen as less and less important to scientific knowledge.

For each of the three models, we can also plot a histogram of the credit produced within the disciplines (figure 2). This reveals that there is a higher skew in the distribution of credit over fields in model 3 (figure 2(c)) versus model 2 (figure 2(b)) versus 1 (figure 2(a)). In other words, credit, relative to the total amount of credit produced, is less evenly distributed across fields as we include additional reputational effects, consistent with the above description of increasing dismissal of certain fields. As is likely unsurprising, though it is not shown in figure 2, fields associated with marginalized groups tend to be on the lower end of the distribution, i.e. they are the ones that are being increasingly dismissed.

Figure 2. Histogram of average credit produced across all disciplines for (a) model 1, (b) model 2, and (c) model 3.