Nanum Jeon
Biographic Data
| ID | 94647 |
|---|---|
| NAME | Nanum Jeon |
| GIVEN NAMES | Nanum |
| FAMILY NAME | Jeon |
| SIGNATURE | JEON N |
| AFFILIATIONS | Upsala College |
| ORCID | 0000-0001-9499-1367 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 8 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Causal Machine Learning
Causal explanation is central to sociological research, shaping both theoretical development and empirical inquiry. This paper argues that causal machine learning—which integrates deductive identification strategies with inductive estimation techniques—offers an analytical approach for modeling complex, nonlinear social processes within the potential outcomes framework. We argue that causal machine learning operates through an iterative feedback …
Swapping Gender Traditionalism
South Korea provides an ideal setting for studying religion and gender because Western and local religions are both prominent, and Confucianist beliefs still shape gender norms. Using the 2018 Korean General Social Survey, this study examines the extent to which two dimensions of gender traditionalism in South Korea-Confucian patriarchal ideology (i.e., belief in the subordination of women for Confucian patriarchy) and separate spheres ideology (…
Untangling perceptions of atypical parents
Objective This study examines how the public perceives of five types of “atypical” parents in the United States—single mothers, single fathers, lesbian couples, gay couples, and adoptive parents—including, critically, the factors that contribute to these perceptions. Background Although a handful of studies have considered attitudes toward atypical parents, virtually no studies have considered why people hold the attitudes they do. In addition, f…
Researcher reasoning meets computational capacity
Researcher reasoning meets computational capacity
Swapping Gender Traditionalism
South Korea provides an ideal setting for studying religion and gender because Western and local religions are both prominent, and Confucianist beliefs still shape gender norms. Using the 2018 Korean General Social Survey, this study examines the extent to which two dimensions of gender traditionalism in South Korea-Confucian patriarchal ideology (i.e., belief in the subordination of women for Confucian patriarchy) and separate spheres ideology (…
Causal Machine Learning
Causal explanation is central to sociological research, shaping both theoretical development and empirical inquiry. This paper argues that causal machine learning—which integrates deductive identification strategies with inductive estimation techniques—offers an analytical approach for modeling complex, nonlinear social processes within the potential outcomes framework. We argue that causal machine learning operates through an iterative feedback …
Untangling perceptions of atypical parents
Objective This study examines how the public perceives of five types of “atypical” parents in the United States—single mothers, single fathers, lesbian couples, gay couples, and adoptive parents—including, critically, the factors that contribute to these perceptions. Background Although a handful of studies have considered attitudes toward atypical parents, virtually no studies have considered why people hold the attitudes they do. In addition, f…
Researcher reasoning meets computational capacity
Swapping Gender Traditionalism
South Korea provides an ideal setting for studying religion and gender because Western and local religions are both prominent, and Confucianist beliefs still shape gender norms. Using the 2018 Korean General Social Survey, this study examines the extent to which two dimensions of gender traditionalism in South Korea-Confucian patriarchal ideology (i.e., belief in the subordination of women for Confucian patriarchy) and separate spheres ideology (…
Causal Machine Learning
Causal explanation is central to sociological research, shaping both theoretical development and empirical inquiry. This paper argues that causal machine learning—which integrates deductive identification strategies with inductive estimation techniques—offers an analytical approach for modeling complex, nonlinear social processes within the potential outcomes framework. We argue that causal machine learning operates through an iterative feedback …
Computational and Text Analysis Methods (2 works) · LGBTQ Health, Identity, and Policy (2 works) · Normative (2 works) · Qualitative Comparative Analysis Research (2 works) · Sociology (2 works) · Advanced Causal Inference Techniques (1 works) · Artificial Intelligence (1 works) · Artificial Intelligence (1 works) · Big data (1 works) · Buddhism (1 works)