December 20, 2022

Podcast

Episode 23: Shirley Malcom – Where Science and Society Meet

By Shirley M. Malcom

Shirley M. Malcom is a trailblazer in the area of broadening participation in science. Currently senior advisor and director of the SEA Change initiative at the American Association for the Advancement of Science, she has long worked to create institutional transformation in support of diversity, equity, and inclusion. 

On this episode, we are delighted to feature her talk from the 2022 Henry and Bryna David lecture in its entirety. This lecture series is sponsored by the National Academies’ Division of Behavioral and Social Sciences and Education and Issues in Science and Technology. In her lecture, she talks about the importance of the behavioral sciences, social sciences, and education in evidence-based public policy. She brings her considerable expertise in public science literacy, issues of diversity, equity, and inclusion, and STEM education to bear on the challenges facing American society.

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Transcript

Lisa Margonelli: Welcome to The Ongoing Transformation, a podcast from Issues in Science and Technology. Issues is a quarterly journal published by the National Academies of Sciences, Engineering, and Medicine and Arizona State University. I’m Lisa Margonelli, editor-in-chief of Issues. We’re breaking from our normal format to feature a recent lecture from a science policy luminary, Dr. Shirley Malcom.

Dr. Malcom is a trailblazer in the area of broadening participation in science. She currently serves as a senior advisor and director of the SEA Change Initiative at the American Association for the Advancement of Science. SEA Change aims to create institutional transformation in support of diversity, equity, and inclusion. In October, Dr. Malcom gave the 2022 Henry and Bryna David lecture, which was sponsored by the National Academy’s Division of Behavioral and Social Sciences and Education and Issues in Science and Technology. In this episode, Dr. Malcom tackles some of the big questions at the heart of science policy. How should science meet societal needs? How can science help solve wicked problems? How can we make science more equitable and inclusive?

Without further ado, here’s Dr. Malcom.

Shirley Malcom: I’m honored to have been invited to present the 2022 Henry and Bryna David Lecture, “Where Science and Society Meet.” This is even more special for me because it’s the 60th anniversary of the Division of Behavioral and Social Sciences and Education of the National Academies. Working with the division has been an important part of my own career. But I must tell you, I am not a behavioral scientist. I’m a biologist. Actually, I’m an ecologist—so maybe I do belong.

My career at AAAS post-academe has included experience as a policy researcher and influencer. I didn’t even know there was such a term as an influencer until we had social media. With occasional adventures into the realm of policymaking, trying to identify and disrupt patterns within the science ecosystem have reinforced bias and promote inequities against members of marginalized groups.

I toyed with the idea of entitling this lecture in a way that actually describes my work, as well as the work of the division. And I thought about calling it “In The Land Of Wicked Problems.” But then, it wasn’t clear if everybody would kind of get what I meant by that. In a 2008 article in Harvard Business Review, “Strategy as a Wicked Problem,” John Camillus discussed how Horst W.J. Rittel and Melvin Webber, professors of design and urban planning at the University of California Berkeley, described wicked problems. In a 1973 article in Policy Science magazine, they noted wicked problems as having lots of causes, as being hard to describe, and as not having a right answer. Such problems occur in social context. There are lots of stakeholders, all of whom are bringing different values, perspectives and priorities, and they all think they’re right. The issues are complex and entangled. And every time you think you have them nailed down, they slip away from you. They’re not that simple country road. They’re more like Spaghetti Junction in Atlanta.

This sounds very much like the issues that have defined my own work and that I have encountered in my volunteer service with panels and committees within the Division of Behavioral and Social Science and Education. Those are the kinds of concerns that one routinely encounters when trying to navigate or inform a path forward in the policy domain. Determining how science can inform policy and what science to tap into to inform a particular policy question is not for the faint of heart. Whatever you do will be wrong. You will find supporters and detractors. It’s inevitable. Please note my use of the term informed policy. That was intentional, and I will come back to that particular aspect later.

So what’s the opposite of a wicked problem? I was wondering about that. And I found out that they’re called tame. Tame problems can be technically very hard to address, but they also tend to be, for lack of a better technical term, less messy. More straightforward. My friend, the late physicist and Nobel Prize recipient, Leon Lederman, used to talk to me about how much more clear physics was when compared to addressing social challenges such as educational inequalities. In 1987, when then US Education Secretary Bill Bennett called out Chicago as having the worst school system in the United States, dived into efforts to try and help, such as focusing on mathematics and science teaching professional development through the Teachers Academy for Mathematics and Science. Contrasting physics as having solutions no matter how hard they might be to come by, he noted that addressing education was much harder because “people are involved.” So everything quickly gets more complicated. And that is where the contrast between wicked and tame problems often begins.

I have never understood why it feels like the physical sciences and mathematical sciences are referred to as hard science, compared to social and behavioral sciences that are often described as soft science. According to Leon’s definitions, experiences and assessment, it ought to be the other way around. So why a Division of Behavioral and Social Sciences and Education? And you should know, I tried to find the origin story. I actually called Carlotta and said, “What’s the origin story for the division?” And she didn’t quite know yet, but thought that it was a nice task to start trying to untangle.

So, as a lover of mystery stories, I decided I would design my own plot line. First, the motive. I asked myself, what was happening in the United States in the early 1960s that might have promoted the development of the division? The organizational configuration is actually quite intentional, in terms of what’s inside of the division—I guess it was the assembly before—but what was actually inside of it. So what might compel the leadership of the National Research Council to enhance its capacity to consider issues subsumed under the division’s umbrella? Opportunity. Perhaps I should ask, what wasn’t happening?

The shock wave of my generation, the baby boomers, we were coming of age. Societal transformation with increased pressure on government to intercede in the issues of the day, supporting civil and human rights, demand and resistance, the integration of transportation and public accommodation, schools, colleges, universities, the Civil Rights Movement, demands for women’s rights and women’s increased participation in the paid workforce, inequalities in educational access, opportunities, entertainment, and the push for legislation in action to address these, all of that was going on. And with the increasing awareness and demands for action, a search for solutions, programs that might be put in place to address such inequities. And then the need for evaluation to actually determine whether or not the programs were working.

The emergence of the so-called Great Society programs during the Johnson administrations. Headstart 1965. Corporation for Public Broadcasting 1967, which gave us Sesame Street. Food Stamp Act of 1964. Voting rights. The passage of the Civil Rights Act of 1964. Increased interest in utilizing science to inform decision making and to advance understanding of complex societal trends. Science could not be the only thing to inform action. There were budgets to consider, as well as politics and reputation on the world’s stage. But science was a powerful tool that could be brought to bear in understanding and reacting to all of the stuff that was going on.

At the time, there were staggering levels of poverty in the United States and civil restlessness, that societal issues such as these were not being addressed. Today, it might seem hard to believe, but the policy rate, the percentage of the population actually living below the poverty levels in 1960 was 22%, over one-fifth of all Americans. And within this figure, there was evidence of staggering inequality, with poverty rates of 17% for whites and 55% for Blacks. That’s 5-5, 55% for Blacks. Thanks to many of the programs put in place, the overall poverty rate in 2021 was about half that of 1960. 11.6%. Not ideal, but a tremendous accomplishment. But the inequality has lingered. 8.1% of white, non-Hispanic Americans, compared with 19.55% of Blacks, 17.1% of Hispanics, and a whopping 24.35% of American Indians and Alaskan natives.

I do not know the internal discussions that led to the organization of the division. I would’ve loved to have been a fly on the wall in that room. But I certainly lived through the times that would make me now, in retrospect, want to have an entity that could more objectively explore and document conditions that fed different lived experiences and opportunities for people, based on gender, gender identity, race, ethnicity, disability, age, geography, and/or place in the society and economy. It was almost a hundred years after the signing of the Emancipation Proclamation, yet in the 1960s, poverty still raged among the descendants of previously enslaved people.

I know that certain components of the division actually date their connection to the academies from much earlier than the 60 year anniversary, such as the work supporting the federal statistical systems. I think it was my first experience as a volunteer when I became connected with the efforts to strengthen the infrastructure of the National Center for Education Statistics, which has been in existence in one form or another since 1867.

You can’t look only at the structural issues that are related to the methodological advances within the agency without also discussing what was and was not being collected, for whom and for what purpose, a meeting of the technical and the societal. At the time of my service, I kept my comments and my focus on the task at hand, the ones that we were being asked to consider, such as access to statistical expertise, greater statistical power within the agency. But now looking back, I wish I had spent some time reflecting on the larger back story, the context of what and how we count, whom we count, why we count, and what to make of what we count, especially in the service of policy.

The origins of counting in the United States are grounded in the origins of the country, since the census, which I consider the backbone of our statistical system, was legally mandated under Article 1, Section 2, Clause 3 of the US Constitution, with the first census after the American Revolution taking place in 1790, under Secretary of State Thomas Jefferson. Counting involved the issue of not only how many but also how and who, as well as the purposes of counting. As you can see from the statement that is actually Clause 3, which you don’t see that much now, this was the clause that contained the three-fifths compromised.

“Three-fifths of all other persons.”

Obviously, these directives were modified under the 14th Amendment, Section 2, when previously enslaved people were directed to be counted as whole numbers. So we went from three-fifths to whole numbers. My ancestors did. But the political and historical roots of the business of counting within the census remain a part of the story, while the ability to improve the counting and unpack the numbers rely on its technical roots. Yet it seems that almost every word of that section of the Constitution continues to be argued. Not so much from a technical perspective, although I am sure there is some of that among the statisticians, but most certainly from a sociopolitical one. The work of NCES has involved not only improving its capacity to count, but who is counted and how, what is counted and what we make of what is counted. What is counted and what we make of what is counted bleed over into the policy concerns, where the science meets society.

At the end of the day, NCES numbers document systematic inequality, differences in rates of expulsions and suspensions, for example. Differences in resources allocated to education, and in performance level of students by state or by demographic groups, differences in courses taken and in courses available to be taken. The technical issues related to small numbers, for example, and the people issues of identity and privacy can and do collide. I contend that the wicked problem of educational inequality require access to and consistent utilization of lenses for diversity, equity, and inclusion. The counting can help us in this. The numbers are necessary, but they are not sufficient. The DEI lenses must be incorporated at every step, in issues framing, historical context, perspectives taking, amassing the research, interpreting the data, and as disaggregated a form as possible, telling the story of different stakeholders and offering recommendations. The lenses you use affect what you see and even what it means to see.

Now, I decided that this is such a hard thing to think about, that I would use an example from a totally different field, and that is that of astronomy, so the different lenses and the different frames for this work.

Now, these are amazing pictures under any circumstances, and I want to give you something pretty to look at. On the far left, you have the picture from the Hubble. That’s an optical picture. On the far right, you have a picture from the James Webb Space Telescope, which is infrared picture. And in between, you have a composite. Now we can see the stuff over in the optical side. We can’t see the stuff in the infrared side. But we can be enabled to see. And when put together, we gained much more information than we would if we only looked at the picture that came from the optical side.

Here you see a picture of the Crab Nebula. This is from the Hubble. And then, here is an image that combines the data from five different telescopes, showing a composite with radio, infrared, optical, ultraviolet, and x-ray. We only see, in the visible band, a very small piece of the spectrum. But putting all of these pieces together, these images from NASA and the European Space Agency, all of a sudden, you gain much more clarity, at the same time that you gain many more questions.

So what is the true representation? What you can see, what you’re enabled to see, and what do astronomers and astrophysicists make of what they see? I understand that it’s a lot easier to have this discussion of seeing different things in connection to looking at a galaxy than it is in engaging discussions around educational opportunities that may flow to different groups by race, gender, ethnicity, or any other demographic characteristic. To quote Leon again, “When people are involved, it gets complicated.”

As mentioned earlier, my career at AAAS is focused on improving the quality of STEM education for all and supporting diversity, equity, and inclusion in science, engineering, technology, mathematics, medicine, and related fields. This includes expanding opportunities, increasing participation and advancing the progress of persons from historically excluded group. In science, equity and excellence are inexorably linked. Our enterprise must have inclusion if we are to have excellence. So my work has been as much about the quality of the science we do, or the engineering that we do, or the innovation we create, as it is about the people who are doing these things.

I have always hoped that quality education for all would provide people with tools of discernment, helping them separate fact from factionated, being able to analyze and argue from evidence. I consider these as critical aspects of good citizenship and good policy making. Those are not the only considerations in policy makings, but it helps when you at least can agree on the facts. I pay attention to the numbers, or at least try to, to the issue of who is doing the science. And I have always fought for data disaggregation, though early in my career, as a Black woman in a field with few of us, I probably disappeared because of cell size.

One mystery that has puzzled me for decades has been that of understanding, what happened to the women in computing? In 1984, women received over 37% of bachelor’s degrees in computer science, and now they receive around 20% of such degrees, a focus on how many does it generally in gender disagreement. We tend to trust our statistical systems, but when the conversation moves to why, the discussions become a bit edgier. In presentations I have given over the years, where I have asked audiences to consider that question, where did the women go, the interactions do not always go well. Some accuse me of urging quotas. I do not. Other audience members have suggested I am pushing social engineering. I am not. Some have suggested lack of interest or lack of intellectual capacity by the women. Not supported by the evidence.

I have found too many instances where even scientifically educated people are more than willing to comment based on their biases than on the research. And that’s disheartening, because one of the things that I have always hoped is that this education will give you better lenses to actually look at the world. But I learned that being educated in science does not guarantee that evidence will trump biases, when addressing issues rooted in so-called societal norms of what is appropriate for members of particular groups.

One of the panels that I served on was that of, that started in 1984, on technology and women’s employment. It’s the same year that women achieved their high watermark for bachelor’s degrees in computing. But the study’s focus looked in a totally different direction, not at women in computing, but that the impact of technological change on a segment of the workforce where women dominated, where it looked like the technological change was going to significantly impact not only the numbers but the character of the jobs that they had. The basic mainstay of women’s employment were clerical positions, typists, secretaries. And these positions were being dramatically impacted by the coming of personal computing and all kinds of changes in telecommunications. So there was concern within the government. This upheaval would have drastic effects on women in the workforce.

It’s important to understand the historical context of these questions. There was severe occupational and hierarchical segregation in the workplace. A woman might be a secretary, but she wasn’t a manager. I don’t even know if enough of you are around back when I was around to remember that help wanted ads were segregated in the newspaper. Help wanted male. Help wanted female. Those such ads were ruled illegal by the Equal Employment Opportunity Commission in 1968. The ruling was challenged. And it was not until 1973 that the Supreme Court upheld the EEOC’s ruling, thus allowing women to apply for jobs generally higher paying, that has previously been open only to men.

Before women had been able to gain a foothold in this more lucrative part of the job market, the clerical and service areas into which they had been segregated were facing major transformation. We could see the technology that was coming. We could see, for example, that stenographers would be made obsolete with voice recognition and other kinds of things that were coming down barreling down the road.

I mentioned it as an example of a technological revolution, but I also want to emphasize the need to look deeper. In this case, how education levels, even within the sector, as well as age and minority status, stratified the opportunity structures even further. There was some good news in the analysis that was being done by the panel. As the nature of work was shifting, there were opportunities for improving the quality of work and for advancements such as upgrading traditional secretarial or typist jobs, to those that we might refer to today as administrative or executive assistants where there’s much more that is actually involved in the work.

But these only work if people had the education that was necessary to take advantage of this or if they had the opportunity for upskilling. Even as the quality might have improved, the size of the sector was decreasing. In addition, the jobs in the sector were often sub-segregated where, for example, Black women who were laid entrants into some of these positions because of racial segregation were entering the sector through back office work, such as data entry. And that was disappearing, as well with the coming of the changes in the technology.

So the panel and its parent committee were grappling with a truly wicked cluster of problems, how to mitigate the worst effects of technological change, which were differentially affecting populations that were already experiencing discrimination, and some within the sector because of their intersectional position affected even more. There were lots of causes to this problem, as well as a lot of stakeholders. Business wanted to make things more efficient. They wanted to cut down on their cost. They wanted all increased productivity. But we also had this huge segment, this huge chunk of mostly women who were in these jobs.

It’s hard to describe what the issue was. Technological change was good, wasn’t it? Women had lots of employment opportunities, didn’t it? And there were no right answers, only things that could make the situation better or worse. The panel and committee could recommend, caution, inform, offer options, but at the end of the day, the policy makers had to navigate this difficult space. And the women and their families, the families they helped support, had to absorb the impacts.

And finally, the set of activities that I was involved in within the division that relate to education. I have been involved across the board in these issues. First, with the National Science Education Standards, the first standards, and a member of that committee, and later as chair of the panel on Barriers and Opportunities to two-year, four-year STEM degrees. And I want to talk about the perspectives gained from these different experiences in terms of different goals.

Advancing science literacy require to support citizenship and opportunities for careers on the one hand, and the development of the STEM workforce on the other hand. Education, science, mathematics, technology and engineering is important in K-12, not only to enable study at higher levels, but critically for preparing everyone to live and work in a society and economy increasingly defined by science and technology. The academies could contribute, and they could influence the national discussion about what and why. Why do we have to have a good quality STEM education? They were perceived as authoritative, non-partisan, and so they could wade into these conversations. But K-12 is tough space to navigate. In the US, there is local control of schools, not a national ministry of education, and sometimes, I think that’s a good thing.

With over 130,000 schools and more than 13,000 school districts, developing standards is only the beginning of the work to be undertaken, and there are also the efforts to advance and gain science community consensus as well as public acceptance. There’s the need to harmonize the standards, for schools, for students with the preparation of teachers, and therefore, with the curricular expectations of higher education and the state licensure apparatus. Materials and resources must also harmonize, as well as the design of instructional spaces, assessment and more, and on and on, the rest of the components of a complex ecosystem that makes up K-12 education.

And given the historical and current inequities in education, DEI lenses are needed for each and every component, while making authoritative statements of what concepts in physics all students should understand. For example, how do you overcome the shortage of highly qualified physics teachers? Or the fact that schools with significant minoritized populations are less likely to even offer advanced coursework? My tenure on the committee ended before we got this far down the road, when I stopped being a panelist and became one of the clients for this work as a member of the National Science Board. And I learned something really important. Where you stand depends on where you sit. When you’re a client, it’s a totally different story than when you’re a panelist. And the questions about equitable implementation have never gone away. They still sit there.

With Barriers and Opportunities, we began with the DEI lens and used those to frame everything, understanding the requirement for diversity of workforce and the realities that pose barriers to full inclusion, focusing considerable attention on two-year institutions because of the disproportionate role in enrolling students of color and women, and the range of curricular options that they offer. We even had to rethink what is meant by success in some of these programs. If a person could take their skills and go out and get a much higher paying job, was this not good if they didn’t complete their degree? Or was it good? Focusing on diverse pathways and the need for systemic change to realizing these. Discussing how the culture of science itself could be a barrier, and how the assets of a diverse student’s population are needed to support workforce needs and spur innovation.

There are many examples that I could have used. I chose these because I was directly involved in them. And this has basically been a life journey that I have brought you along. Because there’s an incredible body of work for the Division of Behavioral and Social Sciences and Education over its 60-year history. And almost any report that I could have pulled from the virtual shelves shares the need for DEI lenses, and that’s going to be even more true going into the future.

Science often enjoys a very uneasy relationship with society, more so for some segments than other. Science and technology drive change. Not everybody likes change. They help solve problems, and they answer questions. But almost any question science answers leads to new questions. An idea captured in the title, of Vannevar Bush’s Endless Frontier, and a recommendation that you should just probably use is boiler plate to go into any report that you decide to do.

Some people are discomforted by change. And they don’t like the fact that the pace of change is accelerated. Others, because of past experiences of recent encounters, just don’t trust science. Still others, not seeing those who look like them among the doers of science or the makers of technology, tune out the message. Why should I listen to those people? And then there are people who reject the message when it does not support their beliefs or their biases. Too often, these groupings break along demographic or factional lines. At that point, our value in policy discussions is diminished, but it’s even more important. A response I have seen in some policy spaces is to paint over the work. Make it so general as to be meaningless, or to aggregate and generalize in ways that distort the narrative, thus running the risk of sacrificing the integrity of the work. What is it you’re really trying to say?

Where DEI effects exist, they should be called out whether or not they were requested. Because if they are part of the story, they are part of the story. Being diverse and equity aware rather than race or gender blind is not biased, where these elements are critical to honest narrative and truth to the science. We must push back on those who resist the call out of differential outcomes, no matter what coded language they may use. Astronomy doesn’t have this problem. Wait a minute. Yes, it does. In the last decade of survey, one of the problems that was actually called out for astronomy was a lack of diversity among the people who actually do the work.

It takes courage and commitment, to the integrity of the work above all, a DEI lens in framing, a DEI lens in the questions asked, the panel members recruited. In some of those early reports that are looked at, they didn’t have much diversity. And I worry about their recommendations. The papers commissioned, the landscape scanned, the history considered, the voices of agreement and dissent fully welcome. Empathy with the stakeholder informant and policy communities. The next 60 years are going to be harder, especially as the problems become more wicked.

I began this lecture with reflection on what and how we count. And looking at the mission of the census, spirit says that we see the mandate to provide quality data that helps leaders and decision makers maintain our representative form of democracy. And it ends with the role of science education in giving people tools and ways of thinking needed to preserve our democracy, as well as for those who choose the skills to advance science and innovation in support of global goals, at the evolution of our democracy, as we meet the challenges of the future that await us. Thank you.

Lisa Margonelli: What a fantastic lecture. We are delighted to feature Dr. Shirley Malcom. To find more of her work, visit seachange.aaas.org. Subscribe to The Ongoing Transformation wherever you get your podcasts. You can email us at [email protected] with any comments or suggestions. If you enjoy conversations like this, visit issues.org, where you can subscribe to our magazine and find more essays. Thanks to our podcast producer, Kimberly Quach, and audio engineer, Shannon Lynch. I’m Lisa Margonelli, editor-in-chief of Issues in Science and Technology, and thank you for listening.