Calvin Liang

10 papers receiving 312 citations

Calvin Liang's Hit Papers

Embracing Four Tensions in Human-Computer Interaction Research with Marginalized People 2021 · 158 citations
1580+1+3Years since publication50100150

Peers

Calvin Liang
Comparison fields: 5 of 69
  • Human-Computer Interaction 109
  • Applied Psychology 29
  • Communication 36
  • Management of Technology and Innovation 24
  • Health Informatics 4
Replace Juan F. Maestre with:
Juan F. Maestre United States
Elizabeth Stowell United States
Heather Faucett United States
Wendy Roldan United States
C. Estelle Smith United States
Jessica L. Feuston United States
Teresa Almeida United Kingdom
Marguerite Barry Ireland
Lauren M. Britton United States
Gopinaath Kannabiran United States
Calvin Liang relative to Juan F. Maestre United States Juan F. Maestre's profile →
Citations per field
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Juan F. Maestre · 1×
Citations per year

Countries citing papers authored by Calvin Liang

Since Specialization
Citations

This map shows the geographic impact of Calvin Liang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Calvin Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Calvin Liang more than expected).

Fields of papers citing papers by Calvin Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Calvin Liang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Calvin Liang. The network helps show where Calvin Liang may publish in the future.

Co-authors

The 25 scholars most cited alongside Calvin Liang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Calvin Liang Line = papers co-authored together Calvin Liang links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1
Embracing Four Tensions in Human-Computer Interaction Research with Marginalized People
Hit paper breakdown →
2021158
2 201938
3 201936
4 202030
5 202125
6 202013
7 20237
8 20205
9 20214
10 20241
11 20260
12 20260
13 20260
14 20260

About Calvin Liang

Calvin Liang is a scholar working on Human-Computer Interaction, Sociology and Political Science, Clinical Psychology, Gender Studies and Communication, having authored 14 papers that have together received 317 indexed citations. Recurring topics across this work include Innovative Human-Technology Interaction (8 papers), Impact of Technology on Adolescents (3 papers), Gender, Feminism, and Media (2 papers), Sexuality, Behavior, and Technology (2 papers), Sex work and related issues (1 paper), Technology Use by Older Adults (1 paper), Mental Health and Patient Involvement (1 paper) and Data Visualization and Analytics (1 paper). The work is most often cited by research in Human-Computer Interaction (109 citations), Applied Psychology (29 citations), Communication (36 citations), Management of Technology and Innovation (24 citations) and Health Informatics (4 citations). Calvin Liang has collaborated with scholars based in United States, Ecuador and Argentina. Frequent co-authors include Sean A. Munson, Julie A. Kientz, Emily Zeng, Arpita Bhattacharya, Ashley Marie Walker, Jeremy Birnholtz, Kathryn E. Ringland, Ashley Kraus, David J. Inwards-Breland and Kym Ahrens. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, International journal of communication, ACM Transactions on Computer-Human Interaction, First Monday and Journal of Adolescent Health.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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