The stability of androgynous names: Dynamics of gendered naming practices in the United States 1880–2016

Seguin, Charles; Julien, Chris; Zhang, Yongjun

doi:10.1016/j.poetic.2020.101501

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…Nevertheless, people often name their children in ways that (consciously or unconsciously) signal gender, racial/ethnic, religious, and even class membership. 10,[16][17][18] Other times, they resist these associations by choosing ambiguous names for their children 16,17 , or by changing their own names later in life. The aggregate result of these choices is an imperfect cultural consensus around the gendered, racialized, and other associations of many names.…”

mentioning

confidence: 99%

“…Other names, like "Leslie," are commonly used for both women and men, resulting in weaker demographic correlations and less social signaling information. 16,19 Most name-based demographic imputation tools are simple naive-Bayes classifiers. 18,20 They start with a reference dataset of name-gender or name-race records like baby names from the US Social Security Administration and define the probability that a name belongs to each gender or racial group as the proportion of people with that name in each group in the reference data.…”

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confidence: 99%

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Name-based demographic inference and the unequal distribution of misrecognition

2023

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Academics and companies increasingly draw on large datasets to understand the social world, and name-based demographic ascription tools are widespread for imputing information like gender and race that are often missing from these large datasets. These approaches have drawn criticism on ethical, empirical, and theoretical grounds. Employing a survey of all authors listed on articles in sociology, economics, and communications journals in the Web of Science between 2015 and 2020, we compared self-identified demographics with name-based imputations of gender and race/ethnicity for 19,924 scholars across four gender ascription tools and four race/ethnicity ascription tools. We find substantial inequalities in how these tools misgender and misrecognize the race/ethnicity of authors, distributing erroneous ascriptions unevenly among other demographic traits. Because of the empirical and ethical consequences of these errors, scholars need to be cautious with the use of demographic imputation. We recommend five principles for the responsible use of name-based demographic inference.

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confidence: 99%

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confidence: 99%

Name-based demographic inference and the unequal distribution of misrecognition

2023

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“…Nevertheless, people often name their children in ways that (consciously or unconsciously) signal gender, racial/ethnic, religious, and even class membership. 9,[15][16][17] Other times, they resist these associations by deliberately choosing ambiguous names for their children 15,16 , or by changing their own names to have different associations later in life. The aggregate result of these choices is an imperfect cultural consensus around the gendered, racialized, and other associations of many names.…”

Section: Why Are Demographics Correlated With Names?mentioning

confidence: 99%

“…Still other names, like "Leslie," are commonly used for both women and men, resulting in weaker demographic correlations between gender and name and less social signaling information. 15,18…”

Section: Why Are Demographics Correlated With Names?mentioning

confidence: 99%

Name-Based Demographic Inference and the Unequal Distribution of Misrecognition

Lockhart¹,

King²,

Munsch³

2022

Preprint

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Academics and companies increasingly draw on large datasets to understand the social world. Name-based demographic ascription tools are widespread for imputing information like gender and race that are often missing from these large datasets, but these approaches have drawn criticism on ethical, empirical, and theoretical grounds. Employing a survey of all authors listed on articles in sociology, economics, and communications journals in the Web of Science between 2015 and 2020, we compared self-identified demographics with name-based imputations of gender and race/ethnicity for 19,924 scholars across four gender ascription tools (genderize.io, M3-inference, R’s `predictrace` and `gender` packages) and four race/ethnicity ascription tools (ethnicolor’s Florida and North Carolina voter models, and R’s `predictrace` and wru packages). We find substantial inequalities in how these tools misgender and misrecognize the race/ethnicity of authors, distributing erroneous ascriptions unevenly along other demographic traits. Because of the empirical and ethical consequences of these errors, scholars need to be cautious with the use of name-based demographic imputation, particularly when studying subgroups. We recommend five principles for the responsible use of name-based demographic ascription.

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“…A large body of research shows that individuals’ names can signal meaningful demographic characteristics and features of identity, such as age, class, gender, and race (e.g., Bertrand and Mullainathan 2004; Gaddis 2017a, 2017b; Gerhards and Hans 2009; Goldstein and Stecklov 2016; Johfre 2020; Lieberson and Mikelson 1995; Olivetti and Paserman 2015; Sue and Telles 2007). This scholarship, in turn, draws on the insight that given names are selected through meaningful cultural processes unfolding in relation to social context (e.g., Alford 1987; Elchardus and Siongers 2011; Lieberson 2000; Lieberson and Bell 1992; Seguin, Julien, and Zhang 2021; Zelinsky 1970). For example, giving the name “Mary” to a child born in a small Christian community located in a predominantly Muslim country provides telling information about the parents’, community’s, and, likely, child’s culture and beliefs.…”

mentioning

confidence: 99%

Language Models in Sociological Research: An Application to Classifying Large Administrative Data and Measuring Religiosity

Jensen

Karell

Tanigawa-Lau

et al. 2021

Sociological Methodology

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Computational methods have become widespread in the social sciences, but probabilistic language models remain relatively underused. We introduce language models to a general social science readership. First, we offer an accessible explanation of language models, detailing how they estimate the probability of a piece of language, such as a word or sentence, on the basis of the linguistic context. Second, we apply language models in an illustrative analysis to demonstrate the mechanics of using these models in social science research. The example application uses language models to classify names in a large administrative database; the classifications are then used to measure a sociologically important phenomenon: the spatial variation of religiosity. This application highlights several advantages of language models, including their effectiveness in classifying text that contains variation around the base structures, as is often the case with localized naming conventions and dialects. We conclude by discussing language models’ potential to contribute to sociological research beyond classification through their ability to generate language.

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The stability of androgynous names: Dynamics of gendered naming practices in the United States 1880–2016

Cited by 6 publications

References 24 publications

Name-based demographic inference and the unequal distribution of misrecognition

Name-based demographic inference and the unequal distribution of misrecognition

Name-Based Demographic Inference and the Unequal Distribution of Misrecognition

Language Models in Sociological Research: An Application to Classifying Large Administrative Data and Measuring Religiosity

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