Alina Arseniev-Koehler
Dados Biográficos
| ID | 189048 |
|---|---|
| NOME | Alina Arseniev-Koehler |
| PRENOMES | Alina |
| SOBRENOME | Arseniev-Koehler |
| ASSINATURA | ARSENIEV-KOEHLER A |
| AFILIAÇÕES | Department of Sociology, Purdue University, West Lafayette, IN, USA |
| ORCID | 0000-0003-2544-5607 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 10 |
| TOTAL DE CITAÇÕES | 62 |
| TOTAL COMO AUTOR | 10 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2021 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2025 |
| ÍNDICE H | 5 |
Disease frames and their consequences for stigma and medical research funds
Illnesses are often understood as criminal acts, as medically treatable conditions, or through metaphors of battles and journeys. Theorists suggest that frames vary across diseases and over time in systematic ways, and that frames have concrete consequences for the distribution of resources. But data limitations have prevented scholars from testing these hypotheses. We combine word embeddings and regression analysis to examine four frames for 104…
Meaning in Hyperspace
Word embeddings are language models that represent words as positions in an abstract many-dimensional meaning space. Despite a growing range of applications demonstrating their utility for sociology, there is little conceptual clarity regarding what exactly embeddings measure and whether this matches what we need them to measure. Here, we fill this theoretical gap by clarifying how cultural meaning can be understood in spatial terms. We argue tha…
Talk of Family
We develop a novel application of machine learning and apply it to the interview transcripts from the American Voices Project (N = 1,396), using discourse atom topic modeling to explore social class variation in the centrality of family in adults' lives. We take a two-phase approach, first analyzing transcripts at the person level and then at the line level. Our findings suggest that family, as represented by talk, is more central in the lives of…
Gendered Patterns in Manifest and Latent Mental Health Indicators Among Suicide Decedents
Objectives. To investigate differences in the documentation of mental health symptomology between male and female suicide decedents in the 2003–2020 US National Violent Death Reporting System (NVDRS). Methods. Using information on 271 998 suicides in the 2003–2020 NVDRS, we evaluated precoded mental health–related variables and topic model–derived latent mental health themes in the law enforcement and coroner or medical examiner death narratives …
Theoretical Foundations and Limits of Word Embeddings
Measuring meaning is a central problem in cultural sociology and word embeddings may offer powerful new tools to do so. But like any tool, they build on and exert theoretical assumptions. In this paper, I theorize the ways in which word embeddings model three core premises of a structural linguistic theory of meaning: that meaning is coherent, relational, and may be analyzed as a static system. In certain ways, word embeddings are vulnerable to t…
School, Studying, and Smarts
In this article, we apply computational word embeddings to a 200-million-word corpus of American print media (1930–2009) to examine how education-relevant gender stereotypes changed as women’s educational attainment caught up with and eventually surpassed men’s. This case presents a rare opportunity to observe how cultural components of the gender system transform alongside the reversal of an important pattern of stratification. We track six ster…
The Stigma of Diseases
Why are some diseases more stigmatized than others? And, has disease stigma declined over time? Answers to these questions have been hampered by a lack of comparable, longitudinal data. Using word embedding methods, we analyze 4.7 million news articles to create new measures of stigma for 106 health conditions from 1980 to 2018. Using mixed-effects regressions, we find that behavioral health conditions and preventable diseases attract the stronge…
Machine Learning as a Model for Cultural Learning
Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis - neural word embeddings - can be interpreted as a formal model for cultu…
All Roads Lead to Polenta
In the process of retelling information, individuals often inadvertently transform it to be more consistent with their cultural schemas. We explore the long‐term cultural change inherent in this process, focusing on utterances about cultural tastes as our case study (e.g., music, food, and outdoor hobbies). We use a word embedding model to simulate a “telephone game” where each actor partially hears an utterance, uses their cultural schemas to gu…
Aggression, Escalation, and Other Latent Themes in Legal Intervention Deaths of Non-Hispanic Black and White Men
Objectives. To investigate racial/ethnic differences in legal intervention‒related deaths using state-of-the-art topic modeling of law enforcement and coroner text summaries drawn from the 2003–2017 US National Violent Death Reporting System (NVDRS). Methods. Employing advanced topic modeling, we identified 8 topics consistent with dangerousness in death incidents in the NVDRS death narratives written by public health workers (PHWs). Using logist…
The Stigma of Diseases
Why are some diseases more stigmatized than others? And, has disease stigma declined over time? Answers to these questions have been hampered by a lack of comparable, longitudinal data. Using word embedding methods, we analyze 4.7 million news articles to create new measures of stigma for 106 health conditions from 1980 to 2018. Using mixed-effects regressions, we find that behavioral health conditions and preventable diseases attract the stronge…
Machine Learning as a Model for Cultural Learning
Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis - neural word embeddings - can be interpreted as a formal model for cultu…
Meaning in Hyperspace
Word embeddings are language models that represent words as positions in an abstract many-dimensional meaning space. Despite a growing range of applications demonstrating their utility for sociology, there is little conceptual clarity regarding what exactly embeddings measure and whether this matches what we need them to measure. Here, we fill this theoretical gap by clarifying how cultural meaning can be understood in spatial terms. We argue tha…
Theoretical Foundations and Limits of Word Embeddings
Measuring meaning is a central problem in cultural sociology and word embeddings may offer powerful new tools to do so. But like any tool, they build on and exert theoretical assumptions. In this paper, I theorize the ways in which word embeddings model three core premises of a structural linguistic theory of meaning: that meaning is coherent, relational, and may be analyzed as a static system. In certain ways, word embeddings are vulnerable to t…
All Roads Lead to Polenta
In the process of retelling information, individuals often inadvertently transform it to be more consistent with their cultural schemas. We explore the long‐term cultural change inherent in this process, focusing on utterances about cultural tastes as our case study (e.g., music, food, and outdoor hobbies). We use a word embedding model to simulate a “telephone game” where each actor partially hears an utterance, uses their cultural schemas to gu…
Talk of Family
We develop a novel application of machine learning and apply it to the interview transcripts from the American Voices Project (N = 1,396), using discourse atom topic modeling to explore social class variation in the centrality of family in adults' lives. We take a two-phase approach, first analyzing transcripts at the person level and then at the line level. Our findings suggest that family, as represented by talk, is more central in the lives of…
Aggression, Escalation, and Other Latent Themes in Legal Intervention Deaths of Non-Hispanic Black and White Men
Objectives. To investigate racial/ethnic differences in legal intervention‒related deaths using state-of-the-art topic modeling of law enforcement and coroner text summaries drawn from the 2003–2017 US National Violent Death Reporting System (NVDRS). Methods. Employing advanced topic modeling, we identified 8 topics consistent with dangerousness in death incidents in the NVDRS death narratives written by public health workers (PHWs). Using logist…
Gendered Patterns in Manifest and Latent Mental Health Indicators Among Suicide Decedents
Objectives. To investigate differences in the documentation of mental health symptomology between male and female suicide decedents in the 2003–2020 US National Violent Death Reporting System (NVDRS). Methods. Using information on 271 998 suicides in the 2003–2020 NVDRS, we evaluated precoded mental health–related variables and topic model–derived latent mental health themes in the law enforcement and coroner or medical examiner death narratives …
All Roads Lead to Polenta
In the process of retelling information, individuals often inadvertently transform it to be more consistent with their cultural schemas. We explore the long‐term cultural change inherent in this process, focusing on utterances about cultural tastes as our case study (e.g., music, food, and outdoor hobbies). We use a word embedding model to simulate a “telephone game” where each actor partially hears an utterance, uses their cultural schemas to gu…
Aggression, Escalation, and Other Latent Themes in Legal Intervention Deaths of Non-Hispanic Black and White Men
Objectives. To investigate racial/ethnic differences in legal intervention‒related deaths using state-of-the-art topic modeling of law enforcement and coroner text summaries drawn from the 2003–2017 US National Violent Death Reporting System (NVDRS). Methods. Employing advanced topic modeling, we identified 8 topics consistent with dangerousness in death incidents in the NVDRS death narratives written by public health workers (PHWs). Using logist…
Machine Learning as a Model for Cultural Learning
Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis - neural word embeddings - can be interpreted as a formal model for cultu…
School, Studying, and Smarts
In this article, we apply computational word embeddings to a 200-million-word corpus of American print media (1930–2009) to examine how education-relevant gender stereotypes changed as women’s educational attainment caught up with and eventually surpassed men’s. This case presents a rare opportunity to observe how cultural components of the gender system transform alongside the reversal of an important pattern of stratification. We track six ster…
The Stigma of Diseases
Why are some diseases more stigmatized than others? And, has disease stigma declined over time? Answers to these questions have been hampered by a lack of comparable, longitudinal data. Using word embedding methods, we analyze 4.7 million news articles to create new measures of stigma for 106 health conditions from 1980 to 2018. Using mixed-effects regressions, we find that behavioral health conditions and preventable diseases attract the stronge…
Talk of Family
We develop a novel application of machine learning and apply it to the interview transcripts from the American Voices Project (N = 1,396), using discourse atom topic modeling to explore social class variation in the centrality of family in adults' lives. We take a two-phase approach, first analyzing transcripts at the person level and then at the line level. Our findings suggest that family, as represented by talk, is more central in the lives of…
Gendered Patterns in Manifest and Latent Mental Health Indicators Among Suicide Decedents
Objectives. To investigate differences in the documentation of mental health symptomology between male and female suicide decedents in the 2003–2020 US National Violent Death Reporting System (NVDRS). Methods. Using information on 271 998 suicides in the 2003–2020 NVDRS, we evaluated precoded mental health–related variables and topic model–derived latent mental health themes in the law enforcement and coroner or medical examiner death narratives …
Theoretical Foundations and Limits of Word Embeddings
Measuring meaning is a central problem in cultural sociology and word embeddings may offer powerful new tools to do so. But like any tool, they build on and exert theoretical assumptions. In this paper, I theorize the ways in which word embeddings model three core premises of a structural linguistic theory of meaning: that meaning is coherent, relational, and may be analyzed as a static system. In certain ways, word embeddings are vulnerable to t…
Disease frames and their consequences for stigma and medical research funds
Illnesses are often understood as criminal acts, as medically treatable conditions, or through metaphors of battles and journeys. Theorists suggest that frames vary across diseases and over time in systematic ways, and that frames have concrete consequences for the distribution of resources. But data limitations have prevented scholars from testing these hypotheses. We combine word embeddings and regression analysis to examine four frames for 104…
Meaning in Hyperspace
Word embeddings are language models that represent words as positions in an abstract many-dimensional meaning space. Despite a growing range of applications demonstrating their utility for sociology, there is little conceptual clarity regarding what exactly embeddings measure and whether this matches what we need them to measure. Here, we fill this theoretical gap by clarifying how cultural meaning can be understood in spatial terms. We argue tha…
Computer Science (5 obras) · Psychology (5 obras) · Social Psychology (5 obras) · Sociology (5 obras) · Artificial Intelligence (4 obras) · Medicine (4 obras) · Psychiatry (4 obras) · Epistemology (3 obras) · Political science (3 obras) · Social and Cultural Dynamics (3 obras)