Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Identity and a Model of Investment in Applied Linguistics
2015536 citationsRon Darvin, Bonny NortonAnnual Review of Applied Linguisticsprofile →
Investment and motivation in language learning: What's the difference?
Exploring AI-mediated informal digital learning of English (AI-IDLE): a mixed-method investigation of Chinese EFL learners’ AI adoption and experiences
2024106 citationsGuangxiang Liu, Ron Darvin et al.Computer Assisted Language Learningprofile →
Unpacking the role of motivation and enjoyment in AI-mediated informal digital learning of English (AI-IDLE): A mixed-method investigation in the Chinese context
202464 citationsGuangxiang Liu, Ron Darvin et al.Computers in Human Behaviorprofile →
The need for critical digital literacies in generative AI-mediated L2 writing
202522 citationsRon DarvinJournal of Second Language Writingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Ron Darvin'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 Ron Darvin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ron Darvin more than expected).
This network shows the impact of papers produced by Ron Darvin. 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 Ron Darvin. The network helps show where Ron Darvin may publish in the future.
Co-authorship network of co-authors of Ron Darvin
This figure shows the co-authorship network connecting the top 25 collaborators of Ron Darvin.
A scholar is included among the top collaborators of Ron Darvin based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Ron Darvin. Ron Darvin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Darvin, Ron. (2025). The need for critical digital literacies in generative AI-mediated L2 writing. Journal of Second Language Writing. 67. 101186–101186.22 indexed citations breakdown →
Liu, Guangxiang, Ron Darvin, & Chaojun Ma. (2024). Unpacking the role of motivation and enjoyment in AI-mediated informal digital learning of English (AI-IDLE): A mixed-method investigation in the Chinese context. Computers in Human Behavior. 160. 108362–108362.64 indexed citations breakdown →
6.
Liu, Guangxiang, Ron Darvin, & Chaojun Ma. (2024). Exploring AI-mediated informal digital learning of English (AI-IDLE): a mixed-method investigation of Chinese EFL learners’ AI adoption and experiences. Computer Assisted Language Learning. 38(7). 1632–1660.106 indexed citations breakdown →
Darvin, Ron, et al.. (2019). La clase social y las alfabetizaciones digitales desiguales de la juventud / Social class and unequal digital literacies of youth. Revista de educación. 29–53.2 indexed citations
Darvin, Ron & Bonny Norton. (2015). Identity and a Model of Investment in Applied Linguistics. Annual Review of Applied Linguistics. 35. 36–56.536 indexed citations breakdown →
19.
Darvin, Ron & Bonny Norton. (2014). Transnational Identity and Migrant Language Learners: The Promise of Digital Storytelling. The Journal of Teaching and Learning. 2(1).57 indexed citations
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.