Machine-Assisted Coding is a computational methodology in which algorithms and machine learning techniques are employed to support, enhance, or streamline the process of assigning codes to textual data, distinguishing it from fully manual or fully automated coding by emphasizing human-machine collaboration in qualitative data analysis.
18
Publications
967
Citations
52
Authors
18
Institutions
Publications per year
2017–2026
12
Parents
Publications
18
1–5 of 18
Authors
52
- GZ
1
h-index
- DD
1
h-index
- TL
1
h-index
- JL
1
h-index
- LC
1
h-index
1–5 of 52
Institutions
18
Beijing Academy of Artificial Intelligence
Beijing, China
1 h-index
3 publications
108 citations
Zhaoqing University
Zhaoqing, China
1 h-index
1 publications
18 citations
Stanford University
Stanford, United States
1 h-index
1 publications
33 citations
Karlsruhe Institute of Technology
Karlsruhe, Germany
1 h-index
2 publications
134 citations
University of Arizona
Tucson, United States
1 h-index
2 publications
36 citations
Arizona State University
Tempe, United States
1 h-index
2 publications
64 citations
University of California, San Francisco
San Francisco, United States
1 h-index
1 publications
18 citations
Aristotle University of Thessaloniki
Thessaloniki, Greece
1 h-index
4 publications
84 citations
1–8 of 18
Venues
- arXiv (Cornell University)
Repository · 2 publications · 45 citations
2
h-index
- Policy & Internet
1 publications · 18 citations
1
h-index
- Journalism & Mass Communication Quarterly
JMCQ · 1 publications · 93 citations
1
h-index
- ETS Research Report Series
1 publications · 13 citations
1
h-index
- Socius Sociological Research for a Dynamic World
1 publications · 18 citations
1
h-index
1–5