work

Societies grow and change all the time, but it can be tough to think about big-picture shifts when you’re living through the practical details of the day to day. Take the recent popularity of large language models (LLMs). In the short term, we face important sociological questions about how they fit into the norms of everyday life. Is it cheating to use an LLM to help you write, or to generate new ideas? How will new kinds of automation change work, or will they take jobs away?

These are important questions, but it is also useful to take a step back and think about what rapid developments in technology might do to our foundational social relationships and core beliefs. I was fascinated by a recent set of studies published in PNAS that suggest automated work and LLMs could even change the way we think about religion.

“draw an illustration of a church slowly dissolving into a series of zeros and ones, like computer code in The Matrix”

In the article “Exposure to Automation Explains Religious Declines,” authors Joshua Conrad Jackson, Kai Chi Yam, Pok Man Tang, Chris G. Sibley, and Adam Waytz review the findings from five studies. In one, their analysis of longitudinal data across 68 countries from 2006 to 2019 finds nations with higher stocks of industrial robots also tend to have lower proportions of people who say religion is an important part of their daily lives in surveys.

Figure 1 from Jackson et al. (2023) demonstrating nations with a higher stock of industrial robots also express lower rates of religiosity, on average. You can read the full notes at the open-access article here.

I was most surprised by the results of their fifth study—an experiment teaching people about recent advances in science and AI. Respondents who read about the capabilities of LLMs like ChatGPT showed “greater reductions in religious conviction than learning about scientific advances” (8).

The authors suggest one reason for this pattern is that “people may perceive AI as having capacities that they do not ascribe to traditional sciences and technologies and that are uniquely likely to displace the instrumental roles of religion” (2). This is important for us, whether or not you’re personally religious, because religion is a socially powerful force – people use shared beliefs to accomplish things in the world and solve problems, even to cope with hardships like losing a job.

But these results show that new changes in technology, like the advent of LLMs, might be expanding people’s imaginations about what we can do and achieve, possibly even changing the core beliefs that are central to their lives over the long term.

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow his work at his website, on Twitter, or on BlueSky.

It is hard to keep up habits these days. As the academic year starts up with remote teaching, hybrid teaching, and rapidly-changing plans amid the pandemic, many of us are thinking about how to design new ways to connect now that our old habits are disrupted. How do you make a new routine or make up for old rituals lost? How do we make them stick and feel meaningful?

Social science shows us how these things take time, and in a world where we would all very much like a quick solution to our current social problems, it can be tough to sort out exactly what new rules and routines can do for us.

For example, The New York Times recently profiled “spiritual consultants” in the workplace – teams that are tasked with creating a more meaningful and communal experience on the job. This is part of a larger social trend of companies and other organizations implementing things like mindfulness practices and meditation because they…keep workers happy? Foster a sense of community? Maybe just keep the workers just a little more productive in unsettled times?

It is hard to talk about the motives behind these programs without getting cynical, but that snark points us to an important sociological point. Some of our most meaningful and important institutions emerge from social behavior, and it is easy to forget how hard it is to design them into place.

This example reminded me of the classic Social Construction of Reality by Berger and Luckmann, who argue that some of our strongest and most established assumptions come from habit over time. Repeated interactions become habits, habits become routines, and suddenly those routines take on a life of their own that becomes meaningful to the participants in a way that “just is.” Trust, authority, and collective solidarity fall into place when people lean on these established habits. In other words: on Wednesdays we wear pink.

The challenge with emergent social institutions is that they take time and repetition to form. You have to let them happen on their own, otherwise they don’t take on the same same sense of meaning. Designing a new ritual often invites cringe, because it skips over the part where people buy into it through their collective routines. This is the difference between saying “on Wednesdays we wear pink” and saying

“Hey team, we have a great idea that’s going to build office solidarity and really reinforce the family dynamic we’ve got going on. We’re implementing: Pink. Wednesdays.”

All of our usual routines are disrupted right now, inviting fear, sadness, anger, frustration, and disappointment. People are trying to persist with the rituals closest to them, sometimes to the extreme detriment of public health (see: weddings, rallies, and ugh). I think there’s some good sociological advice for moving through these challenges for ourselves and our communities: recognize those emotions, trust in the routines and habits that you can safely establish for yourself and others, and know that they will take a long time to feel really meaningful again, but that doesn’t mean they aren’t working for you. In other words, stop trying to make fetch happen.

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow his work at his website, on Twitter, or on BlueSky.

Sociologists
studying emotion have opened up the inner, private feelings of anger, fear,
shame, and love to reveal the far-reaching effects of social forces on our most
personal experiences. This subfield has given us new words to make sense of shared
experiences: emotional labor in our professional lives, collective
effervescence at sporting events and concerts, emotional capital as a resource
linked to gender, race, and class, and the relevance of power in shaping
positive and negative emotions.

Despite
these advances, scholars studying emotion still struggle to capture emotion
directly. In the lab, we can elicit certain emotions, but by removing context,
we remove much of what shapes real-life experiences. In surveys and interviews,
we can ask about emotions retrospectively, but rarely in the moment and in
situ.

One
way to try to capture emotions as they unfold in all of their messy glory is
through audio diaries (Theodosius 2008). Our team set out to use audio
diaries as a way to understand the emotions of hospital nurses—workers on the
front lines of healthcare. We asked nurses to make a minimum of one recording
after each of 6 consecutive shifts. Some made short 10-minute recordings. Some
talked for hours in the midst of beeping hospital machines and in break rooms,
while walking to their cars, driving home, and as they unplugged after a long
day. With the recorders out in the world, we couldn’t control what they
discussed. We couldn’t follow-up with probing questions or ask them to move to
a quieter location to minimize background noise.

But what this lack of control gave us was a trove of emotions and reflections, experienced and processed while recording. One fruitful way to try to distill these data, we found, was through visuals. We created wavelength visualizations in order to augment our interpretation of diary transcripts. Pairing the two reintroduces some of the ‘texture’ of spoken word often lost in the transcription process (Smart 2009:296). The following is from our new article in the journal, Qualitative Research (Cottingham and Erickson Forthcoming).

In this first segment, Tamara (all participant names are pseudonyms) describes a memorable situation in which a patient’s visitor assumed that Tamara was a lower-level nursing aid rather than a registered nurse (the full event is discussed in greater detail in Cottingham, Johnson, and Erickson 2018). This caused her to feel “ticked” (angry), which is the word she uses after a quick, high-pitched laugh that peaks the wavelength just after the 30-s mark (Figure 1). The wavelength peak just after the 1:15 mark is as she says the word ‘why’ with notable agitation in ‘I’m not sure why. Maybe cuz I’m Black. I don’t know.’

Figure 1. Tamara’s “Ticked” Segment (shift 2, part 1)

We can compare Figure 1 that visualizes Tamara’s feelings of
anger with the visualization of emotion in Figure 2. “Draining” is the
description Tamara gives at the beginning of this second segment. The peak just
after the 15-second mark is from a breathy laugh as she describes her sister “who
has MS is sitting on the bedside commode” when she gets home from work. After
the 45-second mark, she has a similar breathy laugh but in conjunction with the
word ‘compassionate’ as she says ‘I’m trying to be as empathetic and
compassionate as I want to be, but I know I’m really not. So I feel kinda
crappy, guilty maybe about that.’ Just before the 1:30 mark she draws out the
words ‘draining’ and ‘frustrating’ before finishing: ‘because you leave it and
you come home to it…you know…yeah.’ We can see that the segment ends with
longer pauses, muted remarks, and sighs, suggesting low energy and representing
the drained feelings she expresses, particularly in comparison to the lively
energy seen in the first segment when she discusses feeling angry.

Figure 2. Tamara’s “Draining” Segment (shift 2, part 2)

A second example comes from Leah, recorded while driving to work. Here she is angry (“pissed off”) because she has to work on a day that she was not originally scheduled to work. This segment is visualized in the waveform shown in Figure 3.

Figure 3. Leah’s ‘Righteous Indignation’ Segment (shift 2, part 1)
Figure 4. Leah’s ‘I Don’t Want to Stay’ Segment (shift 2, part 3)

In contrast to her discussion of being pissed off and working to ‘retain enough righteous indignation’ to confront her boss later (in figure 3), we see a different wavelength visualization in her second segment (figure 4). In that segment, she describes her lack of enthusiasm for continuing the shift. She reflects on this lack of desire (‘I don’t want to stay’) by stepping outside her own feelings and contrasting them with the dire circumstances of her young patient. This reflexivity leads her to conclude that she has reached the limits of her ability to be compassionate.

To
be sure, waveform visualizations are only meaningful in tandem with what our nurses say. And they do not
provide definitive proof of certain emotions over others. They can’t fully
identify the sighs, deep inhales, uses of sarcasm, or other subtle features of
spoken diary entries. They do, however, offer some insight into how speed,
pitch, and pauses correspond to different emotional expressions and, arguably,
levels of emotional energy (Collins 2004) that vary across time and interactions.

While
there is little that can serve as a substitute for hearing the recordings
directly, the need to protect participants’ confidentiality compels us to turn
to other means to convey the nuances of these verbalizations. Visualization of
wavelengths, in combination with transcripts, can lend themselves to further
qualitative interpretation of these subtleties, conveying the dynamics of a
segment to others who do not have direct access to the recordings themselves.

Check
out the full, open-access article on this topic here and more on the experiences of nurses
here.

Marci Cottingham is assistant professor of sociology at the University of Amsterdam. She researches emotion and inequality broadly and their connection to healthcare and biomedical risk. She is a 2019-2020 visiting fellow at the HWK Institute for Advanced Study. More on her research can be found here: www.uva.nl/profile/m.d.cottingham

References:

Collins, Randall.
2004. Interaction Ritual Chains. Princeton, New Jersey: Princeton
University Press.

Cottingham,
Marci D. and Rebecca J. Erickson. Forthcoming. “Capturing Emotion with Audio
Diaries.” Qualitative Research. https://doi.org/10.1177/1468794119885037

Cottingham,
Marci D., Austin H. Johnson, and Rebecca J. Erickson. 2018. “‘I Can Never Be
Too Comfortable’: Race, Gender, and Emotion at the Hospital Bedside.” Qualitative
Health Research
28(1):145–158. https://doi.org/10.1177/1049732317737980

Smart,
Carol. 2009. “Shifting Horizons: Reflections on Qualitative Methods.” Feminist
Theory
10(3):295–308.

Theodosius,
Catherine. 2008. Emotional Labour in Health Care: The Unmanaged Heart of
Nursing
. NY: Routledge.
[

What do college graduates do with a sociology major? We just got an updated look from Phil Cohen this week:

These are all great career fields for our students, but as I was reading the list I realized there is a huge industry missing: data science and analytics. From Netflix to national policy, many interesting and lucrative jobs today are focused on properly observing, understanding, and trying to predict human behavior. With more sociology graduate programs training their students in computational social science, there is a big opportunity to bring those skills to teaching undergraduates as well.

Of course, data science has its challenges. Social scientists have observed that the booming field has some big problems with bias and inequality, but this is sociology’s bread and butter! When we talk about these issues, we usually go straight to very important conversations about equity, inclusion, and justice, and rightfully so; it is easy to design algorithms that seem like they make better decisions, but really just develop their own biases from watching us.

We can also tackle these questions by talking about research methods–another place where sociologists shine! We spend a lot of time thinking about whether our methods for observing people are valid and reliable. Are we just watching talk, or action? Do people change when researchers watch them? Once we get good measures and a strong analytic approach, can we do a better job explaining how and why bias happens to prevent it in the future?

Sociologists are well-positioned to help make sense of big questions in data science, and the field needs them. According to a recent industry report, only 5% of data scientists come out of the social sciences! While other areas of study may provide more of the technical skills to work in analytics, there is only so much that the technology can do before companies and research centers need to start making sense of social behavior. 

Source: Burtch Works Executive Recruiting. 2018. “Salaries of Data Scientists.” Emphasis Mine

So, if students or parents start up the refrain of “what can you do with a sociology major” this fall, consider showing them the social side of data science!

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow his work at his website, on Twitter, or on BlueSky.

On January 31, The New York Times responded to a letter from Kimberly Probolus, an American Studies PhD candidate, with a commitment to publish gender parity in their letters to the editor (on a weekly basis) in 2019. This policy comes in the wake of many efforts to change the overwhelming overrepresentation of men in the position of “expert” in the media, from the Op-Ed project to womenalsoknowstuff.com (now with a sociology spinoff!) to #citeblackwomen.

The classic sociology article “Doing Gender,” explains that we repeatedly accomplish gender through consistent, patterned interactions. According to the popular press and imagination — such as Rebecca Solnit’s essay, Men Explain Things to Me — one of these patterns includes men stepping into the role of expert. Within the social sciences, there is research on how gender as a performance can explain gender disparities in knowledge-producing spaces.

Women are less likely to volunteer expertise in a variety of spaces, and researchers often explain this finding as a result of self-esteem or confidence. Julia Bear and Benjamin Collier find that, in 2008 for example, only 13% of contributors to Wikipedia were women. Two reasons cited for this gender disparity were a lack of confidence in their expertise and a discomfort with editing (which involves conflict). Likewise, studies of classroom participation have consistently found that men are more likely than women to talk in class — an unsurprising finding considering that classroom participation studies show that students with higher confidence are more likely to participate. Within academia, research shows that men are much more likely to cite themselves as experts within their own work.

This behavior may continue because both men and women are sanctioned for behavior that falls outside of gender performances. In research on salary negotiation, researchers found that women can face a backlash when they ask for raises because self-promotion goes against female gender norms. Men, on the other hand, may be sanctioned for being too self-effacing.

Source: Fortune Live Media, Flickr CC

Knowledge exchange on the Internet may make the sanctions for women in expert roles more plentiful. As demonstrated by the experiences of female journalists, video game enthusiasts, and women in general online, being active on the Internet carries intense risk of exposure to trolling, harassment, abuse, and misogyny. The social science research on online misogyny is also recent and plentiful.

Photo Credit: Sharon Mollerus, Flickr CC

Social media can also be a place to amplify the expertise of women or to respond to particularly egregious examples of mansplaining. And institutions like higher education and the media can continue to intervene to disrupt the social expectation that an expert is always a man. Check out the “Overlooked” obituary project for previously underappreciated scientists and thinkers, including the great sociologist Ida B. Wells.

For more on gendered confidence in specific areas, such as STEM, see more research on Gendering Intelligence.

Originally Posted at There’s Research On That

Jean Marie Maier is a graduate student in sociology at the University of Minnesota. She completed the Cultural Studies of Sport in Education MA program at the University of California, Berkeley, and looks forward to continuing research on the intersections of education, gender, and sport. Jean Marie has also worked as a Fulbright English Teaching Assistant in Gumi, South Korea and as a research intern at the American Association of University Women. She holds a BA in Political Science from Davidson College.

Originally Posted at Discoveries

Many different factors go into deciding your college major — your school, your skills, and your social network can all influence what field of study you choose. This is an important decision, as social scientists have shown it has consequences well into the life course — not only do college majors vary widely in terms of earnings across the life course, but income gaps between fields are often larger than gaps between those with college degrees and those without them. Natasha Quadlin finds that this gap is in many ways due to differences in funding at the start of college that determine which majors students choose.

Photo by Tom Woodward, Flickr CC

Quadlin draws on data from the Postsecondary Transcript Study, a collection of over 700 college transcripts from students who were enrolled in postsecondary education in 2012. Focusing on students’ declared major during their freshman year, Quadlin analyzes the relationship between the source of funding a student gets — loans, grants, or family funds — and the type of major the student initially chooses — applied versus academic and STEM versus non-STEM. She finds that students who pay for college with loans are more likely to major in applied non-STEM fields, such as business and nursing, and they are less likely to be undeclared. However, students whose funding comes primarily from grants or family members are more likely to choose academic majors like sociology or English and STEM majors like biology or computer science.

In other words, low- and middle-income students with significant amounts of loan debt are likely to choose “practical” applied majors that more quickly result in full-time employment. Conversely, students with grants and financially supportive parents, regardless of class, are more likely to choose what are considered riskier academic and STEM tracks that are more challenging and take longer to turn into a job. Since middle- to upper-class students are more likely to get family assistance and merit-based grants, this means that less advantaged students are most likely to rely on loans. The problem, Quadlin explains, is that applied non-STEM majors have relatively high wages at first, but very little advancement over time, while academic and STEM majors have more barriers to completion but experience more frequent promotions. The result is that inequalities established at the start of college are often maintained throughout people’s lives.

Jacqui Frost is a PhD candidate in sociology at the University of Minnesota and the managing editor at The Society Pages. Her research interests include non-religion and religion, culture, and civic engagement.

The Washington Post recently covered a leaked email exchange from the University of Maryland in which the school’s Mock Trial team assistant coach lamented the “mediocre” to “poor” performance of Latinx students and asked if any of them had to be included to satisfy diversity requirements for the makeup of the team. While an embarrassing situation for both the students and the authors of the e-mail exchange, this event reflects recent research in law and society that addresses questions of race, gender, immigrant background, and inequality in the legal profession and among law school students.

While law firms put a high value on American law school training, not everyone gets the same benefits from a legal education. Students of color who are the children of immigrants earn less after graduating from law school. Implicit biases can also determine who gets hired at elite jobs and exert pressure on these students while they are in school. This recent incident at Maryland shows how these patterns affect minority students’ everyday experiences in law school.

Our analysis emphasizes the persistent inequality in the median income level among lawyers in the United States. There are sizable differences in earnings across race and gender. We have found that, while immigrant status alone is not always negatively associated with income, it does compound the income disadvantage of immigrants when combined with race and gender.

Prepared by the authors. Source: U.S. Census and American Community Survey 1970-2010. All values are in 2010 U.S. dollars. These charts refer to individuals who were in the labor force, employed in the legal profession, and worked in the legal services industry.

One issue that has come up in the course of our study is the question of whether law school matters. We considered that the first generation of immigrants is likely educated abroad, whereas the second generation is potentially educated in the U.S. Although we do not have a direct measure of whether the immigrants in our sample have received their law degree from an American or a foreign law school, previous research has found that age at immigration is a valid proxy measure for place of education.

We do find a slight income advantage for those educated in the United States. However, as the incident in Maryland might suggest, education alone may not determine future professional success as a lawyer. Because the law school environment emphasizes network building and socialization through extra-curricular activities, such as moot court or mock trial, students who are not selected for these opportunities (especially on the basis of race!), may be additionally disadvantaged when looking for a job as well as in the marketplace.

Alisha Kirchoff is a PhD student and Associate Instructor of sociology at Indiana University. Her research is in law and society, political sociology and comparative sociology. She is also currently working as a digital content producer for Contexts Magazine.

Vitor Martins Dias is a graduate student in the Department of Sociology at Indiana University-Bloomington. Prior to leaving Brazil to pursue his LL.M. at Indiana University Maurer School of Law, he received his LL.B. and LL.M., respectively, from Centro Universitário do Pará and São Paulo Law School of Fundação Getúlio Vargas. He is broadly interested in the areas of development, the legal profession, law and society, political economy, political sociology, and inequality.

Originally posted at Gender & Society

Last summer, Donald Trump shared how he hoped his daughter Ivanka might respond should she be sexually harassed at work. He said“I would like to think she would find another career or find another company if that was the case.” President Trump’s advice reflects what many American women feel forced to do when they’re harassed at work: quit their jobs. In our recent Gender & Society article, we examine how sexual harassment, and the job disruption that often accompanies it, affects women’s careers.

How many women quit and why?  Our study shows how sexual harassment affects women at the early stages of their careers. Eighty percent of the women in our survey sample who reported either unwanted touching or a combination of other forms of harassment changed jobs within two years. Among women who were not harassed, only about half changed jobs over the same period. In our statistical models, women who were harassed were 6.5 times more likely than those who were not to change jobs. This was true after accounting for other factors – such as the birth of a child – that sometimes lead to job change. In addition to job change, industry change and reduced work hours were common after harassing experiences.

Percent of Working Women Who Change Jobs (2003–2005)

In interviews with some of these survey participants, we learned more about how sexual harassment affects employees. While some women quit work to avoid their harassers, others quit because of dissatisfaction with how employers responded to their reports of harassment.

Rachel, who worked at a fast food restaurant, told us that she was “just totally disgusted and I quit” after her employer failed to take action until they found out she had consulted an attorney. Many women who were harassed told us that leaving their positions felt like the only way to escape a toxic workplace climate. As advertising agency employee Hannah explained, “It wouldn’t be worth me trying to spend all my energy to change that culture.”

The Implications of Sexual Harassment for Women’s Careers  Critics of Donald Trump’s remarks point out that many women who are harassed cannot afford to quit their jobs. Yet some feel they have no other option. Lisa, a project manager who was harassed at work, told us she decided, “That’s it, I’m outta here. I’ll eat rice and live in the dark if I have to.

Our survey data show that women who were harassed at work report significantly greater financial stress two years later. The effect of sexual harassment was comparable to the strain caused by other negative life events, such as a serious injury or illness, incarceration, or assault. About 35 percent of this effect could be attributed to the job change that occurred after harassment.

For some of the women we interviewed, sexual harassment had other lasting effects that knocked them off-course during the formative early years of their career. Pam, for example, was less trusting after her harassment, and began a new job, for less pay, where she “wasn’t out in the public eye.” Other women were pushed toward less lucrative careers in fields where they believed sexual harassment and other sexist or discriminatory practices would be less likely to occur.

For those who stayed, challenging toxic workplace cultures also had costs. Even for women who were not harassed directly, standing up against harmful work environments resulted in ostracism, and career stagnation. By ignoring women’s concerns and pushing them out, organizational cultures that give rise to harassment remain unchallenged.

Rather than expecting women who are harassed to leave work, employers should consider the costs of maintaining workplace cultures that allow harassment to continue. Retaining good employees will reduce the high cost of turnover and allow all workers to thrive—which benefits employers and workers alike.

Heather McLaughlin is an assistant professor in Sociology at Oklahoma State University. Her research examines how gender norms are constructed and policed within various institutional contexts, including work, sport, and law, with a particular emphasis on adolescence and young adulthood. Christopher Uggen is Regents Professor and Martindale chair in Sociology and Law at the University of Minnesota. He studies crime, law, and social inequality, firm in the belief that good science can light the way to a more just and peaceful world. Amy Blackstone is a professor in Sociology and the Margaret Chase Smith Policy Center at the University of Maine. She studies childlessness and the childfree choice, workplace harassment, and civic engagement.