Author: Lisa Singh

Dr. Lisa Singh is a Professor in the Department of Computer Science at Georgetown University and a Research Professor in the Massive Data Institute (MDI).

Categories
Blog Post

Helping Our Junior Faculty – Now Is the Time

During this time of crisis, we have seen many women emerge as strong leaders. Women have been prominent as leaders of nations and as care-leaders. Almost 80% of healthcare workers are women, and over 80% provide social services (see TimesUpFoundation). Even though they have been warriors during this time of crisis, we need to pause and think about the additional burden we have placed on them and how much we are expecting from them. We need to try to give a helping hand. 

I have been outspoken about issues related to women in computer science. In this blog post, I am going to branch out beyond my discipline and call on universities and senior faculty to find creative ways to help our junior faculty who are trying to balance demanding academic careers and young kids that need their attention during COVID-19.

Research and statistics are emerging that show parents of younger children, especially women, falling behind with their research agendas during this COVID-19 crisis (see articles below).  As an academic and a mom of two older children (high school and college), I am very worried about this issue. How do we in academia help junior faculty with dependents? How can we help give them options so they do not fall behind or feel guilty about spending time with their young kids when childcare is not available? I am attempting to be gender-neutral since I have a lot of colleagues who are male and spend a lot of time with childcare. However, there is research that shows on average moms spend more time with their kids than dads  (see Sani and Tres, 2017 as an example), so we need to understand that this problem may be worse for female faculty. To add to the complexity of the challenge, female faculty only made up 26% of the tenured faculty in 2017 (see Kelly, 2019). How can we improve this ratio if we do not support women, our warrior women, during this crisis? 

Here I throw out a few ideas and possible ways to help. I do not think there is a magic bullet for every person, but if there are a pool of strategies, perhaps subsets of them can be used to support junior faculty with dependents (JFD) who are more impacted during these difficult times. 

1. Allow JFD to extend their tenure clock without penalty: This one is a no brainer. Letting everyone who does not have tenure delay their tenure clock to ease the pressure they are facing is important. Many universities, including Georgetown, have already done this. Of course, we will not understand whether women or faculty of color will use this more and possibly fall more behind until we are a year or two out. But irrespective, this needs to be an option.  

2. To maintain research productivity, allow JFD to delay or reduce teaching obligations: This recommendation is likely one of the more controversial ones, but probably the one that would help the most. Because faculty with young kids need to spend much of their day with their children, teaching during the day can be very challenging. I propose giving junior faculty the option of delaying one course they are teaching until after they receive tenure or possibly even just reducing their teaching load. This means that senior faculty will have to pick up additional courses, but in their case, they would teach a little less after universities are fully open again.

If there is no option for reducing teaching schedules, we should be able to make sure that courses taught by JFD are the smaller ones that require less coordination with TAs and/or less grading.

3. Recalibrate funding expectations: For some fields and research areas, the funding may be increasing. However, for most, it is harder to get right now. Expecting that junior faculty will not get additional funding for the next couple of years needs to be the norm. One way senior faculty can help junior faculty is to find more areas of collaboration so that their chance of getting funding increases. With dwindling resources, senior faculty may have a better chance of obtaining funding than those who are just starting their careers. 

4. Proactively limit distractions: Like many faculty I have spoken to, I strongly dislike Zoom. The cost of a meeting is very low, but the toll of having hours and hours of Zoom meetings is very high. Unless a meeting is really important, let’s not have it, or let’s shorten it. For example, do we really need faculty meetings every month? What about a faculty meeting at the beginning of the semester and then again at the end of the semester? Most of the issues that are being discussed can wait or can be resolved by a subcommittee. If a meeting must occur, can we compress it into 30 minutes or an hour? We have to proactively pause and determine whether or not a meeting is really necessary. There are always time constraints, but while faculty are caring for young children, the constraints are harder and more complicated. If meetings cannot be eliminated, the expectation for JFD should be “try to attend”, not “you must attend.” 

5. In line with limiting distractions, take these JFD off of committees: Not having committee assignments until there is normal child care available will not destroy a department. Yes, that means senior faculty will need to do a little more. But this is about salvaging the mental health of our JFD. As a senior faculty member, this means that my workload will increase. But I assume if I help my colleagues during their time of need, they will help later when others need their help. 

6. Survey all the faculty to understand their concerns and needs: Every university community has its own unique issues. My list is based on issues I have heard through my network of colleagues and friends. However, each university needs to understand its own faculty. Toward that end, set up a (short) survey and get ideas about the largest concerns faculty have and their proposed solutions. It is amazing how many good ideas can emerge by asking those who are most impacted – they have probably been trying to come up with solutions anyways. 

7. Proactively provide stress relief and mental health support: Faculty, particularly faculty in STEM, are notoriously bad at asking for help. A university cannot force mental health support for faculty, but setting up help specific to faculty needs and publicizing it to faculty is important. Another option is to assign all JFD a senior mentor from a different department who had a similar family profile (young kids pre-tenure, etc.) to have a one session chat. This allows these junior faculty a connection outside of his/her department. This can be important if there are political issues within a department. Also, no one wants to believe he/she needs help. By having this informal chat, if help really is needed, the mentor can make suggestions, possibly averting a larger issue later in the year.

We can also help faculty organize “coffee chats” where parents struggling with the same issues of work/life balance can build a community amongst themselves. They can share their challenges and discuss how they have dealt with different issues. Some departments have very few JFD and would not be able to find others in similar situations without help from senior faculty or administration.

8. Help identify viable child care options: Faculty may need an hour of child care support when they teach their classes. Having a pool of child care providers that can be called on to help during class times (especially if we are on-campus in the fall) is very important. As an example, Georgetown has child care under normal circumstances, but it may not be available in its traditional form in the fall. Let’s find a way to utilize the existing infrastructure to help faculty for short periods of time through the day. The format may need to be outdoors in a one-on-one setting or for a socially distanced walk, but thinking through this issue will help many faculty. 

9. Promote JFD work: Since JFD are less likely to participate in conferences and networking during this time, find ways to feature their work and highlight their research. This may be as simple as creating some articles for the university website. It may also be nice to have some video interviews or new faculty research spotlights. Some departments are very good at this, but this is a place where universities can really make a difference. 

We are in a crisis. I am a senior faculty member and I find it challenging to manage my new schedule. Junior faculty with small kids or those caring for elderly parents may be struggling significantly more. I have no doubt that all these JFD and warrior women will figure out how to manage juggling everything during this crisis. However, wouldn’t it be nice if we were proactive and helped them thrive, not just survive.  As senior faculty and administrators, let’s find innovative ways to support them. Let’s set an example of caring for others in our community and making small sacrifices for the greater good. Let’s show that universities know how to lead during times of crisis. Let’s give a helping hand.

Women and research productivity:

Categories
Blog Post

Establishing Norms for Social Media Usage in Research – We All Need to Participate

As a computer scientist, I have always been fascinated by constructing useful insight from large-scale data. When I was a junior faculty member, I wanted to design “clever” algorithms that were efficient. I may have been atypical because I was less connected to the computational task and more connected to the data. I wanted to understand what the data represented, how it was generated, what processing had been done on it, and then develop new ways to describe and use it. Thinking about traditional computational/data mining tasks (association rules, dynamic clustering, anomaly detection) and new ones (stable alliance, prominent actors, bias, and event detection) was a fun mathematical puzzle. I was also very partial to algorithms that used certain data structures (different representations of the data for more efficient processing), particularly graphs (what many other disciplines would call ‘networks’).

Once I learned all I could about the data, I typically moved on. This focus on different types of data may explain how I stumbled into interdisciplinary research. From dolphin observational data to experimental medical data to streaming financial/purchase data, my focus was on developing algorithms that highlighted some aspect of the data that was hidden because of their size or that I needed to keep hidden because of privacy constraints. The algorithms I developed helped other scientists understand their populations better, but even more importantly, the disciplinary theories, ideas, and methods we shared opened up new ways of thinking about old problems in our disciplines.

In the last few years, I have shifted my focus to organic data. Organic data are data that are not designed — survey data are designed by researchers to help research specific hypotheses. Instead they are considered “data in the wild” that are generated in a natural setting. Social media is the largest example of organic data. But instead of just looking at classic and innovative data mining tasks, I have been working with a team of interdisciplinary researchers to help answer social science questions using these data.  What is the impact of news and social media on elections? Or more generally, public opinion? How does social media shape parenting attitudes? How can we use organic data in conjunction with traditional data sources to help forecast forced migration? How can we better understand representativeness of different online populations? How does online conversation drive policy and cultural change? These data are a game changer for social science and public health researchers. They provide a new avenue for learning about human behavior, attitudes, and decision making that are hard to capture in surveys or at scale using traditional ethnographic research methods. 

As I began working on these different questions using organic data sources, it became clear that every project I was involved with was reinventing a “new” methodology – from study design to analysis and interpretation. I understood a great deal about the data, but very little about the social science disciplines that wanted to use these data – from their methodological traditions to their substantive theories. It became clear to me (and many of my collaborators) that a meta-problem existed. These data contained properties that differ from some of the more traditional forms of data used in social, behavioral, and economic (SBE) disciplines and every discipline was developing its own independent standard for using organic data sets. Actually, it would be more accurate to say that every discipline was applying new customized methods for using social media data to help them advance their research. There are cases when the organic data are similar to disciplinary data. In those cases, the scale of the data made it impossible to use traditional approaches for measuring and modeling the data. Every study being conducted was fairly adhoc, focusing on the disciplinary research question instead of a robust, repeatable research design.

So how could/should we tackle this? A group of 12 faculty in 7 disciplines at Georgetown University and University of Michigan (s3mc.org) recognized the need to bring together researchers from different disciplinary traditions to develop frameworks, standards, and designs for extracting significant research value from social media data and other new forms of publicly available text data. Together, we have all begun learning from each other. We have a clearer understanding of why disciplinary questions require social scientists to understand the data generation process completely to answer their research questions, while computer science disciplinary questions do not. Because of the scale of these data and the mismatch in disciplinary research traditions and outputs, it became clear that computer scientists did not always understand how their algorithms would be used by those in other disciplines and those in other disciplines were sometimes using algorithms without really understanding the underlying assumptions and limitations of the algorithms. It became clear that we had a lot to figure out. 

Recently, this group received funding from the National Science Foundation to establish standards for using social media data in SBE disciplines. We are very excited. At a high level our plan is to (1) develop a cross-disciplinary methodology for using social media data in the context of different study designs, (2) create research exemplars (case studies) that use the methodology, (3) build a community of scholars interested in tackling relevant issues, and (4) help develop materials to teach scholars and students how to use these new forms of data in their research. 

It is very exciting for me to be part of this large interdisciplinary team looking at how to responsibly use social media data for social, computer, and data science research. I hope that as the project moves forward, we teach each other the important ideas that our individual disciplines can bring to this problem, while learning how to combine them to generate new ideas and thought that transcend any one of them. My long term vision is for researchers to have the ability to blend data from many sources that capture data at different speeds, resolutions, and forms using new computer science approaches and algorithms that incorporate clear notions of validity and reliability, enabling SBE research that addresses disciplinary questions in a more holistic way – explaining and predicting a wide range of social, behavioral and/or economic phenomena. If this vision is going to become a reality, we are going to have to leave our disciplinary comfort zones, toss out our disciplinary hubris and try a few things that we would normally not consider. I hope in five years I write a blog post about how we came together, converged, and made this vision a reality. That we fundamentally changed how quantitative research involving organic data is approached and conducted.

If you are interested in helping create a roadmap and establish best practices for responsible, replicable, and reliable social media research, connect with us. We need everyone to participate. Below are links to different parts of our project.

  • The Social Science and Social Media Collaborative – https://s3mc.org
  • The NSF Supported Social Media Methodology Project – https://www.smrconverge.org
  • The Google group we are using to help create a broader scholarly community interested in these issues – [email protected]
  • Hashtag for social media – #SMRconverge