May Shih

About

May Shih is a scholar working on Language and Linguistics, Literature and Literary Theory and Education. According to data from OpenAlex, May Shih has authored 7 papers receiving a total of 338 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Language and Linguistics, 4 papers in Literature and Literary Theory and 3 papers in Education. Recurrent topics in May Shih’s work include EFL/ESL Teaching and Learning (4 papers), Second Language Acquisition and Learning (3 papers) and Second Language Learning and Teaching (2 papers). May Shih is often cited by papers focused on EFL/ESL Teaching and Learning (4 papers), Second Language Acquisition and Learning (3 papers) and Second Language Learning and Teaching (2 papers). May Shih collaborates with scholars based in United States and China. May Shih's co-authors include Joy Reid, Kate Parry, Xiaojun Su and Ruth Spack and has published in prestigious journals such as Modern Language Journal, TESOL Quarterly and TESOL Journal.

In The Last Decade

Co-authorship network of co-authors of May Shih i

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Fields of papers citing papers by May Shih

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by May Shih. 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 May Shih. The network helps show where May Shih may publish in the future.

Countries citing papers authored by May Shih

Since Specialization

Citations

This map shows the geographic impact of May Shih'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 May Shih with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites May Shih more than expected).

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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