The rise of machine learning in the academic social sciences
Bibliographic Data
| ID | 20398368 |
|---|---|
| Authors | Charles Rahal (0000-0002-1764-2697, Leverhulme Trust, corresponding author), Megan Verhagen (0000-0003-2746-0309, Leverhulme Trust), David S Kirk (0000-0003-0037-4291, Leverhulme Trust) |
| Year | 2024 |
| Volume | 39 |
| Issue | 2 |
| Pages | 799-801 |
| Publication date | 2024-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | AI & Society (JOURNAL) |
| Journal identifiers | ISSN: 0951-5666 • E-ISSN: 1435-5655 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00146-022-01540-w |
| OpenAlex | W4293250860 |
| Language | EN |
| Citations received | 5 |
| References cited | 7 |
Art · Data science · Machine learning · Mathematics education · Performing arts · Sociology · Visual arts · Computational and Text Analysis Methods · Computer Science · Ethics and Social Impacts of AI · Psychology · Qualitative Comparative Analysis Research · Artificial Intelligence
Understanding the Sequential Pathways of Punitive Supervision and Employee Outcomes
Applying machine learning to gauge the number of women in science, technology, and innovation policy (STIP)
Incorporating Machine Learning into Sociological Model-Building
Evaluating Model Predictive Performance in Confirmatory Factor Analysis with Binary Outcomes Using the InterModel Vigorish
Cheap Learning
Recursive partitioning for heterogeneous causal effects
Integrating explanation and prediction in computational social science
Measuring the predictability of life outcomes with a scientific mass collaboration
Big Data and Their Epistemological Challenge
Where Is the Middle Class? Evidence from 60 Million English Death and Probate Records, 1892-1992
Predicting history
| Citations per year | 2.5 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |