Josh Gupta‐Kagan
Biographic Data
| ID | 10236833 |
|---|---|
| NAME | Josh Gupta‐Kagan |
| GIVEN NAMES | Josh |
| FAMILY NAME | Gupta‐Kagan |
| SIGNATURE | KAGAN J G |
| AFFILIATIONS | University of South Carolina |
| ORCID | 0000-0003-0783-9469 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Predicting less, understanding more
Machine learning algorithms (MLAs) have been deeply embedded across domains, valued for their capacity to analyze large-scale data and their ability to support a range of descriptive and predictive tasks. Despite their versatility, most applications of MLAs to date have focused on individual-level prediction at the expense of broader structural insights. This paper shifts that focus by using MLAs to examine how juvenile and family court intake st…
Reimagining Schools' Role Outside the Family Regulation System
The United States’ family regulation system often begins with well-intentioned professionals making child protection hotline calls, jeopardizing their own ability to work with families and subjecting the families to surveillance. By the system’s own standards, most of this surveillance leads to no meaningful action. Nowhere is this reality more present than in schools. Educational personnel serve as the leading driver of child maltreatment allega…
Operationalizing intake
No prominent works on this page.
Operationalizing intake
Reimagining Schools' Role Outside the Family Regulation System
The United States’ family regulation system often begins with well-intentioned professionals making child protection hotline calls, jeopardizing their own ability to work with families and subjecting the families to surveillance. By the system’s own standards, most of this surveillance leads to no meaningful action. Nowhere is this reality more present than in schools. Educational personnel serve as the leading driver of child maltreatment allega…
Predicting less, understanding more
Machine learning algorithms (MLAs) have been deeply embedded across domains, valued for their capacity to analyze large-scale data and their ability to support a range of descriptive and predictive tasks. Despite their versatility, most applications of MLAs to date have focused on individual-level prediction at the expense of broader structural insights. This paper shifts that focus by using MLAs to examine how juvenile and family court intake st…
Psychology (2 works) · Artificial Intelligence (1 work) · Biology (1 work) · Child protection (1 work) · Child Welfare and Adoption (1 work) · Cognitive Abilities and Testing (1 work) · Cognitive psychology (1 work) · Communication (1 work) · Computer Science (1 work) · Crime Patterns and Interventions (1 work)