Gender representation on UACES 2018 panels

I recently read a blog post on unequal gender representation on academic panels (especially all-male panels, so-called 'manels'). In it, the authors linked to another post that estimated that all-male panels are "getting more rare each year" at the International Studies Association conference.

I realized that after my social network analysis of panels at the 2018 UACES conference and the follow up on the role of gender in the conference's network structure, I already had the data to create a detailed estimate of gender representation on UACES panels.

The caveats

This analysis comes with similar caveats to my previous post on the topic.

First, I coded gender myself, so the results are based on my own interpretation, and not self-identification.

Second, the data is based on the UACES public program, and so does not measure the people who actually participated in person. For example, my panel (Panel 205) is listed as having a 40%/60% female/male split, but was an all-male panel if measured by the people in the room.

Finally, the network is held together by panels that share formal participants, and does not address more relevant - but difficult to measure - aspects such as who went to panels as an audience member.

The findings

As a reminder, for the UACES conference as a whole, the gender split was almost exactly 50-50: 258 participants were women (49.4%) and 264 were men (50.6%).

The headline findings on panel composition mirror this 50-50 split.

Across the 127 panels analyzed, the average (mean) representation overall was 51% women, 49% men. The most common percentage was an exact 50-50 split (16 panels).

Unsurprisingly (for me) these averages masked a lot of variation.

In Figure 1, I show all UACES 2018 panels categorized by the percentage of women participating. There were two all-male panels. In a little more than half of the panels, 50% or more of the participants were women (65 panels, 51%). Around a fifth of panels had more than 80% female participants (24 panels, 19%); of these, 14 featured only women.

Figure 1: UACES 2018 panels by percentage of participants that are women

This information is visualized differently in the network map in Figure 2, where darker green panels have a higher percentage of female participants.

Figure 2: UACES panels connected by shared participants. Greener panels have a higher percentage of participants that are women.

Panels, gender, and network importance: Four angles

How is the gender balance of a panel related to its importance within the network?

In this section I will explore this question using four network statistics: betweenness centrality, closeness centrality, eigenvector centrality, and weighted degree. These three values measure the importance of nodes in different ways, which allows us to take a multifaceted approach. In Figures 3-6, I take the network presented in Figure 2 and re-size the nodes based on their ranking on these statistics (larger nodes have higher scores).

In Figure 3, the size of the nodes depends on their betweenness centrality, a measure of "how often a node appears on shortest paths between nodes in the network." Nodes will a high betweenness centrality are more important to the overall network's connectivity, and removing them is more likely to lead to network fragmentation.

Figure 3: UACES panels re-sized according to their betweenness centrality, a measure of "how often a node appears on shortest paths between nodes in the network (larger nodes have higher centrality). Greener panels have higher percentages of participants that are women.

In Figure 4, the size of the nodes depends on their closeness centrality, "the average distance from a given node to all other nodes in the network."

Figure 4: UACES panels re-sized according to their closeness centrality, which measures average distance from all other nodes (larger nodes have a higher centrality). 

In Figure 5, the size of the nodes depends on their eigenvector centrality, which measures a node's importance based on the importance of the nodes it is connected to; "a node is important if it is linked to by other important nodes."

Figure 5: UACES panels re-sized according to their eigenvector centrality, which measures which measures a node's importance based on the importance of the nodes it is connected to (larger nodes have a higher centrality). 

Finally, in Figure 6, panels are re-sized according to their weighted degree, the number of connections they have with other panels.

Figure 6: UACES panels re-sized according to their weighted degree a measure of how many connections they have with other panels (larger nodes have a higher number of connections). 

Table 1 compares the scores on these four statistics of a number of sub-groups. First, I divided panels into those that had 50% or more female participants, and those that had 49% or less. I then compared the 14 all-female panels with the 14 panels with the lowest percentages of female participants (including the two all-male panels). Finally, for each statistic, I took the top  3, 5, and 10 panels and compared their average percentage of female participants.

Panels with more women had lower betweenness centrality, but were close to even on closeness, eigenvector, and degree. The all-female panels had much lower betweenness than the bottom 14, somewhat lower on closeness, even on degree and significantly higher on eigenvector. The top scoring panels for betweenness and closeness centrality had lower than average female participation, but higher than average for eigenvector centrality and weighted degree.

In summary, different measures of network importance come to different conclusions in this case.

Table 1: Comparing sub-groups on the four network statistics and percentage of participants that are women.

Conclusions

I'd like to emphasize again that these findings should be interpreted with caution: as noted above, the information used to build the network (shared participants) is far from ideal and there could be a number of explanations for the findings (which are not analyzed here).

Hopefully the analysis above can help spark productive conversations on these issues.

UACES 2018 and social network analysis: Exploring gender dimensions

Update: Using the data I compiled for this post, I have created estimates for the percentage of men and women participating in all UACES 2018 panels. That analysis can be found here.

During the 2018 UACES conference on European studies in Bath at the beginning of September, I created several social-network visualizations of panels and participants. The visualizations that focused on participants explored both the number of connections each person had to others, and the 'betweenness centrality' of each person in the network (a measure of their importance for holding the network together).

Two academics who attended the conference - Dr Toni Haastrup and Dr Katharine Wright - asked me about the gender dimensions of these topics (for much more expertise on gender in EU studies, a good place to start is the UACES Gendering EU Studies research network). 

This took additional analysis because the gender of UACES participants was not given on the conference website. So I coded the gender of each participant and added that data to the network maps (like my earlier maps, all participants were anonymized). 

This approach has important limitations: for instance, it is based on my own analysis and not conference participants' self-identified gender. In addition, the results are broad and show only the formal connections between participants, e.g. not those who attended each panel in the audience.

But the results are nevertheless interesting. Below I've focused on two aspects: network centrality and each participant's number of connections.

Network centrality

After removing duplicates, the UACES program listed 522 participants. The gender split was almost exactly 50-50: 258 people were female (49.4%) and 264 were male (50.6%).

The map below shows network participants color-coded according to gender (green is female, blue is male) with node size determined by betweenness centrality, which the network analysis program Gephi defines as "how often a node appears on shortest paths between nodes in the network".

UACES 2018 participants weighted by network centrality. Participants are connected when they participated in the same panel. Green nodes are female participants, blue nodes are male participants. Node size is related to a participant's centrality in the network (betweenness centrality). High-resolution, searchable PDF is available here.

There are obvious differences between the centrality scores of the nodes: the highest betweenness score is 21,203, while over 70% of nodes have a score of zero (because betweenness centrality is a measure of how often a node is on the 'shortest paths' between nodes).

However, there is almost no average difference based on gender, where scores are almost identical (951.1 female participants, 955.3 male).

Focusing only on the 150 participants with a betweenness centrality score above zero, men were more heavily represented in top-20 highest scores, but this evened out in the top 50 and top 150.

Percentage of female/male participants with the highest betweenness centrality scores.

Number of connections

The second way I looked at the issue was through the number of connections participants had with others (weighted degree). The average number of connections overall was 7.4. This average masks a wide range, from a low of 2 connections to a high of 32 connections.

UACES 2018 participants weighted by number of connections (weighted degree). Participants are connected when they participated in the same panel. Green nodes are female participants, blue nodes are male participants. Node size is related to a participant's number of connections (weighted degree). High-resolution, searchable PDF is available here.

On this metric, male participants had an average about 10% higher than their female colleagues (7.1 female vs. 7.8 male). Male participants also made up a high percentage of the top 20 nodes by weighted degree (although this once again was reduced to 50-50 for the top 150).

Percentage of female/male participants with the highest weighted degree scores (number of connections).

Conclusions

A few key findings jump out from this brief analysis.

First, the gender of participants this year was split almost 50-50, in contrast to findings in other disciplines and venues.

Second, there was almost no difference in the average network centrality by gender, and a 10% higher number of connections for male participants.

Third, this broad similarity on average masked a heavy percentage of male participants in the top scores of both centrality and number of connections.

I'd emphasize again that these results should be interpreted with caution: they are preliminary, from one year and one conference, and focus on a single aspect of the dense social networks that exist in these gatherings.

But they do raise interesting further questions:

How have these and other metrics changed over time, in UACES and beyond? What are the differences between areas of focus (e.g. gender studies, environmental policy, foreign policy)? And how do these findings fit into the rich existing academic and societal discussions on gender issues?

I hope this analysis can be an interesting, if small, part of that wider conversation.