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A Roadmap for Inequality Research: Transparency, Intersectionality, and Multiple Measures of Race

By on July 9, 2026 in Articles

Emma Williams-Baron, Aliya Saperstein

Sociological Science July 9, 2026
10.15195/v13.a32

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Most quantitative studies of U.S. inequality rely on single measures of race and do not transparently describe them. However, inconsistencies between measures can yield conclusions that differ both substantively and statistically. We ask: when faced with multiple ways to categorize respondents, how should researchers choose? We conduct intersectional analyses of five inequality outcomes, using the 1979 National Longitudinal Survey of Youth, which offers several measures of self-identification and external classification. Strikingly, we find the survey’s screener race variable, ubiquitous in prior research, is never empirically preferred based on model fit across outcomes spanning the labor market (wages, salary, and unemployment), health (depression), and education (school discipline). Instead, the top-performing measure varies by gender, outcome, and fit statistic. The range of potential researcher decisions and the absence of a clear gold-standard highlights the need for greater transparency and more thoughtful decision-making when researchers operationalize race—whether racial categorization is central to the analysis or included primarily as a control variable. To that end, we offer a roadmap of key considerations inequality researchers can consult when designing their approach.

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Emma Williams-Baron: Department of Sociology, Stanford University. E-mail: emmajwb@stanford.edu.
Aliya Saperstein: Department of Sociology, Stanford University. E-mail: asaper@stanford.edu.

Acknowledgments: We are grateful to our colleagues in the gender and inequality workshops at Stanford University for their helpful comments and suggestions, and to Steve McClaskie for responding to inquiries about the NLSY. Previous versions of this paper were presented at the 2024 American Sociological Association annual meeting and at a 2023 conference on racial inequality in education research hosted by NWEA in Portland, OR. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1656518. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.


Supplemental Materials

Reproducibility Package: Data and code for reproducing the results presented in this article are publicly
available in an Open Science Framework repository here: https://doi.org/10.17605/OSF.IO/K3RZT. Data may also be accessed through the NLSY Investigator site at: https://www.nlsinfo.org/investigator.


  • Citation: Williams-Baron, Emma, Aliya Saperstein. 2026. “A Roadmap for Inequality Research: Transparency, Intersectionality, and Multiple Measures of Race” Sociological Science 13: 825-863.
  • Received: September 20, 2025
  • Accepted: May 18, 2026
  • Editors: Arnout van de Rijt, Kristian B. Karlson
  • DOI: 10.15195/v13.a32


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Leveraging Genomic Data to Document Within-Race Attractiveness Penalties Among Black Americans

By on July 7, 2026 in Articles

Beza Taddess, Luyin Zhang, Sam Trejo

Sociological Science July 7, 2026
10.15195/v13.a31

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In recent years, scholars of racial inequality have increasingly sought to move beyond simply quantifying discrete racial disparities and instead measure social stratification as a function of continuous racialized characteristics that vary both within and between racial groups. In this article, we draw on a sample of genotyped respondents from the Add Health study and construct genetic similarity proportions, individual-level measures that correlate with racialized physical features that vary across the expansive family tree of humanity (skin tone, facial structure, hair texture, etc.). We then investigate the relationship between these proportions and interviewer-rated physical attractiveness among self-identified Black Americans (N=2,087). Our findings highlight the existence of substantial attractiveness penalties related to having higher levels of Sub-Saharan African (as opposed to European) genetic similarity.
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Beza Taddess: Department of Sociology, Princeton University. E-mail: bt7304@princeton.edu
Luyin Zhang: Office of Population Research, Princeton University. E-mail: luyin.zhang@princeton.edu
Sam Trejo: Department of Sociology and Office of Population Research, Princeton University. E-mail: samtrejo@princeton.edu

Acknowledgments: We are grateful to Dalton Conley, Filiz Garip, Iain Mathieson, Ellis Monk, and Marissa Thompson for helpful comments. This research uses data from Add Health, funded by grant P01 HD31921 (Harris) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), with cooperative funding from 23 other federal agencies and foundations. Add Health is currently directed by Robert A. Hummer and funded by the National Institute on Aging cooperative agreements U01 AG071448 (Hummer) and U01AG071450 (Hummer and Aiello) at the University of North Carolina at Chapel Hill. Add Health was designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill. No direct support was received from grant P01 HD31921 for this analysis. Information on obtaining Add Health data is available on the project website. Send correspondence to Sam Trejo, samtrejo@princeton.edu.

Significance Statement: This study provides new evidence on how racialized physical features shape social experiences within a single self-identified racial group. By using genetic similarity proportions—genetic ancestry measures that correlate with physical traits such as skin tone and facial structure—the authors show that Black Americans with higher levels of Sub-Saharan African genetic similarity are systematically rated as less physically attractive. These results reveal a form of racialized disadvantage that operates within racial categories and is not captured by typical survey measures and help explain why traditional surveys report relatively small Black–White attractiveness gaps (whereas real-world behavior shows much larger differences). More broadly, the study offers genetic similarity proportions as a new tool for exploring processes of racialization in contemporary society.


Supplemental Materials

Reproducibility Package: All results needed to evaluate the conclusions in the article are present in the article and/or the Supplementary Materials. All syntax files needed to replicate our main text analyses are available at the following link: https://github.com/luyin-z/attractiveness_penalties. We utilized the restricted Add Health survey and genotype data, which can be accessed by researchers via application at https://data.cpc.unc.edu/projects/2/view.


  • Citation: Taddess, Beza, Luyin Zhang, and Sam Trejo. 2026. “Leveraging Genomic Data to Document Within-Race Attractiveness Penalties Among Black Americans” Sociological Science 13: 802-824.
  • Received: March 24, 2026
  • Accepted: May 13, 2026
  • Editors: Ari Adut, Ellis Monk
  • DOI: 10.15195/v13.a31


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The Double Bind of Precarious Work: Creating Need and Undermining Support

By on July 1, 2026 in Articles

Tyler Woods, Kristen Harknett, Daniel Schneider

Sociological Science July 2, 2026
10.15195/v13.a30

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For most adults in the United States, participation in the labor force is a normative expectation and a pre-requisite for social acceptance and inclusion. Yet, the conditions of low-wage work can breed social isolation by interfering with supportive social ties at and outside of work. Drawing on survey data from The Shift Project, we examine the complex interplay between precarious working conditions and supportive social ties and illuminate a vicious cycle faced by low-wage workers. Precarious work schedule conditions are associated with reduced perceptions of support from social ties and act as a mechanism through which precarious working conditions take a toll on worker well-being. Further, those with precarious work schedules are less likely to benefit from the buffering effect of social support that attenuates the negative consequences of unstable and unpredictable schedules on well-being. Our findings demonstrate negative externalities of precarious working conditions for social support and reveal the double bind of precarious work: schedule instability undermines workers’ social support while simultaneously heightening the need for it.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.


Tyler Woods: Harvard Kennedy School. E-mail: tyler.woods@bain.com.
Kristen Harknett: University of California, Berkeley, Department of Sociology. E-mail: kharknett@berkeley.edu.
Daniel Schneider: Harvard Kennedy School. E-mail: dschneider@hks.harvard.edu.

Acknowledgments: We gratefully acknowledge support from the National Institute on Aging (Grant Nos. R01AG066898 and R56AG081273), the National Institute for Occupational Safety and Health (Grant No. U19OH012293), the Bill and Melinda Gates Foundation (Grant Nos. INV-002665 and INV-016942), the Robert Wood Johnson Foundation (Award No. 74528), and the W.T. Grant Foundation (Grant No. 188043). The findings and conclusions contained within are those of the authors and do not necessarily reflect the positions or policies of these foundations. The authors received excellent research support from Kevin Bruey, Connor Williams, and Alessandra Soto.


Supplemental Materials

Reproducibility Package: Information on accessing the administrative register data and all code used in the analysis is available at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/
OEGXOW.


  • Citation: Woods, Tyler, Kristen Harknett, and Daniel Schneider. 2026. “The Double Bind of Precarious Work: Creating Need and Undermining Support” Sociological Science 13: 772-801.
  • Received: January 5, 2026
  • Accepted: April 28, 2026
  • Editors: Stephen Vaisey, Michael Rosenfeld
  • DOI: 10.15195/v13.a30


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Teacher Sorting and Inequalities in Student Achievement: Unequal Exposures and Differential Returns to Teacher Qualifications

By on June 30, 2026 in Articles

Said Hassan

Sociological Science June 30, 2026
10.15195/v13.a29

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Teachers play a formative role in shaping children’s school experiences and ultimately, their educational outcomes. In this study, I use full population Danish administrative data to explore the consequences of unequal access to qualified teachers in three steps. First, I document strong patterns of teacher–student sorting in Denmark, one of the world’s most equal societies and generous welfare states. In short, teachers from higher socioeconomic backgrounds and with higher prior academic achievements tend to select into schools serving high-achieving children from privileged backgrounds. Second, I investigate the effect of exposure to teachers with different qualifications on students’ test score performance. To facilitate causal estimates, I exploit plausibly exogenous shocks to teacher changes induced by parental leave spells, which, I show, are unrelated to an extensive set of observed classroom characteristics, including student well-being and measures of classroom climate. Third, I explore differentials in the impact of teacher qualifications by students’ socioeconomic background. I find no consistent evidence of differential teacher effects, implying that teacher-induced learning inequalities are mainly driven by unequal exposure to highly qualified teachers, rather than unequal returns to qualifications. This suggests that policies equalizing access to qualified teachers may reduce learning disparities.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.


Said Hassan: Nuffield College, University of Oxford.
E-mail: said.aj.hassan@gmail.com.

Acknowledgments: I am grateful to Richard Breen, David Kirk, Per Engzell, Dirk Witteveen, Anders Hjorth-Trolle, Miriam Gensowski, Janne Jonsson, John Ermisch, Ahmed Tohamy, Anders Holm, and Lars Højsgaard Andersen for their very helpful comments and suggestions on earlier versions. This research was supported by the ROCKWOOL Foundation (grant number 1231).


Supplemental Materials

Reproducibility Package: Information on accessing the administrative register data and all code used in the analysis is available at: https://github.com/s-aj-hassan/Teacher-Sorting-Achievement.


  • Citation: Hassan, Said. 2026. “Teacher Sorting and Inequalities in Student Achievement: Unequal Exposures and Differential Returns to Teacher Qualifications” Sociological Science 13: 747-771.
  • Received: March 30, 2026
  • Accepted: April 17, 2026
  • Editors: Arnout van de Rijt, Herman van de Werfhorst
  • DOI: 10.15195/v13.a29


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The Exception to Women’s Advantage: How Rurality, Red Counties, and the Local Economy Shape Gender Gaps in Educational Attainment

By on June 25, 2026 in Articles

April Sutton, Bernardo Mackenna, Bolun Zhang, Amanda Bosky

Sociological Science June 25, 2026
10.15195/v13.a28

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Rural communities have lagged urban areas in the economic and sociocultural shifts thought to underlie women’s advantage in bachelor’s degree (BA) attainment, such as the expansion of high-status professional jobs and increasing gender egalitarianism. Using nationally representative data (ELS:2002), we bridge the gap between the macroscale factors theorized to drive women’s educational gains and the local environments shaping youth outcomes by analyzing gender patterns in BA attainment across rural and urban high school students. Women who attended high school in metropolitan areas hold a clear BA advantage, but not women who attended nonmetropolitan high schools, where girls earn higher grades than boys yet attain bachelor’s degrees at similar rates. We find that, net of other characteristics, women’s BA advantage is most suppressed in rural counties with strong Republican majorities and limited professional employment opportunities. Overall, our study suggests that women’s BA advantage is geographically uneven and varies across local sociopolitical and economic conditions.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.


April Sutton: UC San Diego-Dept. of Sociology.
E-mail: asutton@ucsd.edu.

Bernardo Mackenna: UC San Diego-Dept. of Sociology.
E-mail: bmackenn@stanford.edu.

Bolun Zhang: UC San Diego-Dept. of Sociology.
E-mail: bolunzhang@zju.edu.cn.

Amanda Bosky: UC San Diego-Dept. of Sociology.
E-mail: abosky@utexas.edu.

Acknowledgments: This research was supported by a National Academy of Education/Spencer postdoctoral fellowship and a UC San Diego Hellman Fellowship awarded to April Sutton. We are grateful for the helpful feedback of anonymous reviewers. The article also benefited from presentations at the NAEd Annual Meeting and Fall Retreat and the Population Association of America.


Supplemental Materials

Reproducibility Package: A replication package, including code, documentation, and links/DOIs to data sources, is available at Zenodo (doi:10.5281/zenodo.17336597). The primary analyses use restricted-access data from the National Center for Education Statistics (NCES) within the Institute of Education Sciences (IES), U.S. Department of Education. These data are available only to approved investigators through the IES/NCES application process (https://ies.ed.gov/about/restricted-use-data). We provide the URLs/DOIs and table identifiers needed to retrieve the publicly available data (e.g., U.S. Census summary files) we used to augment the ELS:2002.


  • Citation: Sutton, April, Bernardo Mackenna, Bolun Zhang, and Amanda Bosky. 2026. “The Exception to Women’s Advantage: How Rurality, Red Counties, and the Local Economy Shape Gender Gaps in Educational Attainment” Sociological Science 13:712-746.
  • Received: May 15, 2025
  • Accepted: October 13, 2025
  • Editors: Arnout van de Rijt, Kristian B. Karlson
  • DOI: 10.15195/v13.a28


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Clickbait Crime News? Metrics and Professional Authority in Local Newsrooms

By on June 22, 2026 in Articles

Jonathan Ben-Menachem

Sociological Science June 22, 2026
10.15195/v13.a27

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Existing research on newsroom metrics documents how journalists construct compatibility between discordant professional and commercial evaluation frameworks. This study examines the underexplored case where metrics validate existing practices. Drawing on interviews with 58 crime journalists in 40 U.S. newsrooms, I find that reporters whose work consistently performed well on audience metrics often defended professional evaluation criteria. Editors facilitated this defense through brokerage, absorbing commercial logics so reporters could experience their work as professionally guided. Market position structured interpretive responses: reporters could avoid metrics, override them, selectively appropriate them, or integrate them into practice. The transition from pageview to subscription regimes reshaped whether concordance was experienced as contaminating or legitimating. Even under concordance, journalists defended professional evaluation criteria.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.


Jonathan Ben-Menachem: Department of Sociology, Columbia University.
E-mail: jb4487@columbia.edu.

Acknowledgments: I gratefully acknowledge feedback from my colleagues in Columbia Sociology’s Qual Lab, my graduate school cohort, and several anonymous reviewers and journal editors. In particular, I thank Ari Galper, Emily Mazo, Tey Meadow, Michael Schudson, Mario Small, and Bruce Western for their generous engagement with this project at various stages. Finally, I thank the journalists who shared their time and experiences with me.


Supplemental Materials


  • Citation: Ben-Menachem, Jonathan. 2026. “Clickbait Crime News? Metrics and Professional Authority in Local Newsrooms” Sociological Science 13: 685-711.
  • Received: March 11, 2026
  • Accepted: May 5, 2026
  • Editors: Ari Adut, Kieran Healy
  • DOI: 10.15195/v13.a27


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Beyond Text: Using AI-Generated Visual Conjoints to Study Gender and Housework Attribution

By on June 16, 2026 in Articles

Léa Pessin, Kevin Munger

Sociological Science June 16, 2026
10.15195/v13.a26

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Despite substantial gender convergence in education and employment, women continue to perform a disproportionate share of housework. We employ a novel visual conjoint experiment to isolate the normative mechanisms underlying this persistent inequality. Using AI-generated photorealistic images, we systematically vary the tidiness of domestic spaces, room type, source of mess, socioeconomic status, and the gender and race/ethnicity of occupants, alongside text describing couples’ employment arrangements. A quota sample of 2,994 U.S. respondents each evaluated five vignettes, yielding 14,970 observations. We find that gender effects operate primarily through responsibility attribution rather than through differential perception of messiness or anticipated social judgment. Women are assigned significantly more cleaning responsibility than men, with the gender penalty concentrated among dual-earner couples. Child-caused mess is perceived as messier than adult-caused mess yet carries reduced social consequences, suggesting that it operates as a legitimating excuse. Our findings suggest that gender equality in paid work is necessary for achieving gender equality in housework, but that it is not sufficient, and that this gap will persist absent changes in normative expectations around responsibility for housework.

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Léa Pessin: Department of Social and Political Science, European University Institute, Fiesole, Italy.
E-mail: lea.pessin@eui.eu.

Kevin Munger: Department of Social and Political Science, European University Institute, Fiesole, Italy.
E-mail: kevin.munger@eui.eu

Acknowledgments: This work has benefited from generous feedback from the Bocconi Dondena Seminar Speaker Series and the LMU Munich Department of Sociology Research Colloquium. Pessin acknowledges funding by the European Union, under the European Research Council grant for the WeEqualize project (grant agreement no. 101117327). Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.


Supplemental Materials

Reproducibility Package: Replication materials including all data and code and documentation necessary to reproduce all empirical results reported in the article can be found at https://github.com/kmunger/Housekeeping_SocSci_Replication.


  • Citation: Pessin, Léa, Kevin Munger, 2026. “Beyond Text: Using AI-Generated Visual Conjoints to Study Gender and Housework Attribution” Sociological Science 13: 661-684.
  • Received: January 20, 2026
  • Accepted: April 14, 2026
  • Editors: Ari Adut, Elizabeth Bruch
  • DOI: 10.15195/v13.a26


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Changing Opportunity: Rising Local Wealth Inequality and Growing Class Gaps in Income Mobility

By on June 15, 2026 in Articles

Manuel Schechtl, Florencia Torche

Sociological Science June 15, 2026
10.15195/v13.a25

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Recent research documents widening class gaps in intergenerational income mobility in the United States. Children from low-income families in more recent cohorts attain lower incomes than their counterparts in earlier cohorts, while no comparable decline is observed among children from high-income families. This study examines whether rising local wealth inequality contributes to this growing class divide in mobility. To do so, it combines newly published estimates of local wealth inequality from GEOWEALTH-US with cohort-based measures of upward mobility from Opportunity Insights. First-difference models reveal a consistent negative association between rising local wealth inequality and declining upward income mobility for children from low-income families, but no comparable association for their high-income peers. These associations are robust to economic and demographic changes, including, critically, changes in income inequality. A decomposition exercise suggests that rising local wealth inequality accounts for roughly one-fifth of the observed increase in class gaps in mobility. Together, the findings identify local wealth inequality as a central dimension of stratification shaping children’s economic opportunities above and beyond income inequality.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.


Manuel Schechtl: University of North Carolina at Chapel Hill.
E-mail: schechtl@unc.edu

Florencia Torche: Princeton University.
E-mail: ftorche@princeton.edu

Acknowledgments: This research was funded by the Volkswagen Foundation.


Supplemental Materials

Reproducibility Package All code necessary to replicate
this study is available in an OSF repository at: https://osf.io/a3vfd/


  • Citation: Schechtl, Manuel, Florencia Torche. 2026. “Changing Opportunity: Rising Local Wealth Inequality and Growing Class Gaps in Income Mobility” Sociological Science 13: 645-660.
  • Received: March 13, 2026
  • Accepted: April 27, 2026
  • Editors: Stephen Vaisey, Herman van de Werfhorst
  • DOI: 10.15195/v13.a25


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Declining Inequality and Persistent Inequality Structures

By on June 10, 2026 in Articles

Soohyun Roh, Nathan Wilmers

Sociological Science June 10, 2026
10.15195/v13.a24

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Prior research finds that rising labor market inequality in the United States was abetted by structural changes in the economy: a consolidation of occupation and organizational bases of advantage; rising within-job inequality; and declining pay and employment in middle-earning jobs. In this article, we revisit these structural changes by asking whether they have been reversed as labor market inequality fell over the last decade. Drawing on restricted-use microdata from the Occupational Employment and Wages Statistics, we find that declining inequality is due to declining inequality in occupation premiums. There has been only a small reversal of consolidation and no decrease in inequality within jobs. Low-wage jobs gained on shrinking middle-earning occupations, further eroding union, manufacturing, and public sector wage premiums. These findings demonstrate a novel configuration of labor market inequality, in which pay rose in low-wage jobs, but underlying inequality structures in the economy persisted.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.


Soohyun Roh: Sloan School of Management, MIT
E-mail: rohs@mit.edu.

Nathan Wilmers: Sloan School of Management, MIT
E-mail: wilmers@mit.edu.

Acknowledgments: Thank you for very helpful comments from the MIT Applied Microeconomics Seminar, University of Maryland Strategy Seminar, NYU Sociology Colloquium, Columbia Center for Wealth and Inequality Seminar, Russell Sage Foundation Visiting Scholar Seminar, Frankfurt School of Finance and Management Seminar, and Stockholm University Department of Economics Seminar. This research was conducted with restricted access to Bureau of Labor Statistics (BLS) data. The views expressed here do not necessarily reflect the views of the BLS or the US government. This research was funded by MIT Sloan. Please direct correspondence to wilmers@mit.edu.


Supplemental Materials

Reproducibility Package: Full replication code is available at https://osf.io/8tbwh. In June 2025, the BLS suspended researcher access to its restricted data. As such, data for the bulk of this analysis are no longer accessible for replication (or to Roh and Wilmers). If the BLS restarts its data access program, then data will be accessible through the application as a visiting researcher.


  • Citation: Roh, Soohyun, Nathan Wilmers. 2026. “Declining Inequality and Persistent Inequality Structures” Sociological Science 13: 614-644.
  • Received: October 16, 2025
  • Accepted: February 26, 2026
  • Editors: Arnout van de Rijt, Cristobal Young
  • DOI: 10.15195/v13.a24


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Family Networks and Childcare Choices: A Predictive Machine Learning Approach

By on June 2, 2026 in Articles

Nicolás Soler, Tom Emery, Agnieszka Kanas

Sociological Science June 2, 2026
10.15195/v13.a23

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How first-time parents arrange childcare has critical implications for their careers and the child’s development. Previous research shows that childcare choices are shaped by family care availability, understood as an additive function of a small set of parental and grandparental characteristics. However, research on family networks suggests that care availability is rather a non-linear, non-additive function of large family networks. We compare the predictive ability of these two perspectives using a machine learning framework and register-based family network data. We find that considering how the child’s great-grandparents, aunts, uncles, and cousins shape care availability, and modeling their influence using more flexible models, provides small yet significant improvements in predictive ability, particularly among more disadvantaged parents. Predictions are driven by parents’ and grandparents’ socioeconomic characteristics, but cousins’ age and daycare use are important yet understudied predictors. Other important understudied predictors include parents’ self-employment, healthcare spending, and timing of daycare uptake.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.


Nicolás Soler: Department of Public Administration and Sociology, Erasmus University Rotterdam.
E-mail: soleralvarezmiranda@essb.eur.nl.

Tom Emery: Department of Public Administration and Sociology, Erasmus University Rotterdam.
E-mail: tom@odissei-data.nl.

Agnieszka Kanas: Department of Public Administration and Sociology, Erasmus University Rotterdam.
E-mail: kanas@essb.eur.nl.


Supplemental Materials

Reproducibility Package: Code to reproduce the results can be found at https://doi.org/10.5281/zenodo.19189668. The data are non-public microdata from Statistics Netherlands that are accessible to accredited researchers under certain conditions (see Statistics Netherlands 2026).


  • Citation: Soler, Nicolás, Tom Emery, Agnieszka Kanas, 2026. “Family Networks and Childcare Choices: A Predictive Machine Learning Approach” Sociological Science 13: 589-613.
  • Received: February 18, 2026
  • Accepted: March 23, 2026
  • Editors: Stephen Vaisey, Michael Rosenfeld
  • DOI: 10.15195/v13.a23


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