Demo Data from VLDB 2026 / reviewers in the wild / expert
Sejal Khatri
dblp:298/4718
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Collaborative and social computing · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational social science and digital humanities › social computing
online community |
0.6 | 1 | 2022 | The social embeddedness of peer production: A comparative qualitative analysis of three Indian language Wikipedia editions · CHI 2022 |
Collaborative and social computing
online communities |
0.6 | 1 | 2022 | The social embeddedness of peer production: A comparative qualitative analysis of three Indian language Wikipedia editions · CHI 2022 |
Collaborative and social computing
peer production |
0.6 | 1 | 2022 | The social embeddedness of peer production: A comparative qualitative analysis of three Indian language Wikipedia editions · CHI 2022 |
Methods — techniques the papers use, named apart from their topics
qualitative interviews · 1.7grounded theory · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | The social embeddedness of peer production: A comparative qualitative analysis of three Indian language Wikipedia editionsabstractWhy do some peer production projects do a better job at engaging potential contributors than others? We address this question by comparing three Indian language Wikipedias, namely, Malayalam, Marathi, and Kannada. We found that although the three projects share goals, technological infrastructure, and a similar set of challenges, Malayalam Wikipedia’s community engages language speakers in contributing at a much higher rate than the others. Drawing from a grounded theory analysis of interviews with 18 community participants from the three projects, we found that experience with participatory governance and free/open-source software in the Malayalam community supported high engagement of contributors. Counterintuitively, we found that financial resources intended to increase participation in the Marathi and Kannada communities hindered the growth of these communities. Our findings underscore the importance of social and cultural context in the trajectories of peer production communities. Sejal Khatri, Aaron D. Shaw, Sayamindu Dasgupta, Benjamin Mako Hill |
CHI | 1 |